AN APPROACH FOR CRIME ANALYSIS USING CLUSTERING …

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AN APPROACH FOR CRIME ANALYSIS USING CLUSTERING

ALGORITHM

A PROJECT REPORT

Submitted by

K.Meghana Chowdary 316126510147

P.Samyuktha 316126510186

V.Ganesh Kumar 316126510180

T.Y.Seshadri Rao 316126510187

in partial fulfillment for the award of the degree

of

BACHELOR OF TECHNOLOGY

IN

COMPUTER SCIENCE ENGINEERING

Under esteemed guidance of

Dr.K.Suresh

(Ass.Proffessor)

DEPARTMENT OF COMPUTER SCIENCE& ENGINEERING

ANIL NEERUKONDA INSTITUTE OF TECHNOLOGY AND

SCIENCES(A)

(Affiliated to Andhra University)

SANGIVALASA, VISAKHAPATNAM -531162

2016 - 2020

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ANIL NEERUKONDA INSTITUTE OF TECHNOLOGY AND

SCIENCES(A)

(Affiliated to Andhra University)

SANGIVALASA, VISAKHAPATNAM-531162

BONAFIDE CERTIFICATE

Certified that this project report “AN APPROACH FOR CRIME ANALYSIS USING

CLUSTERING ALGORITHM” is the bonafide work of “K.Meghana

Chowdary(316126510147),P.Samyuktha(316126510186),V.GaneshKumar(316126510180),T.Y.

Seshadri Rao(316126510187))” who carried out the project work under my supervision

(Dr.R.Sivaranjani) (Dr.K.Suresh)

HEAD OF THE DEPARTMENT PROJECT GUIDE

Designation Designation

Department of Computer Science Department of Computer

and Engineering Science and Engineering

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DECLARATION

This is to certify that the project work entitled “AN APPROACH FOR CRIME ANALYSIS

USING CLUSTERING ALGORITHM” is a bonafide work carried out by K.Meghana

Chowdary, P.Samyuktha, V.Ganesh Kumar, T.Y. Seshadhri Rao as a part of B.TECH

final year 2nd semester of computer science &Engineering of Andhra University,

Visakhapatnam during the year 2016-2020.

We, K.Meghana Chowdary, P.Samyuktha, V.Ganesh Kumar, T.Y.

Seshadhri Rao , of final semester B.Tech., in the department of Computer Science

Engineering from ANITS, Visakhapatnam, hereby declare that the project work entitled AN

APPROACH FOR CRIME ANALYSIS USING CLUSTERING

ALGORITHM is carried out by us and submitted in partial fulfillment of the requirements for

the award of Bachelor of Technology in Computer Science Engineering , under Anil

Neerukonda Institute of Technology & Sciences during the academic year 2016-2020 and has

not been submitted to any other university for the award of any kind of degree.





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ACKNOWLEDGEMENT

The satisfaction and euphoria that accompany the successful completion of task would be incomplete

without the mention of the people who made it possible, whose constant guidance and encouragement always

boosted the morale. We take a great pleasure in presenting a project, which is the result of a studied blend of

both research and knowledge.

We first take the privilege to thank the Head of our Department, Dr.R.Shivaranjani, for permitting

us in laying the first stone of success and providing the lab facilities, we would also like to thank the other

staff in our department and lab assistants who directly or indirectly helped us in successful completion of the

project.

We feel great to thank Dr.K.Suresh , who are our project guides and who shared their valuable

knowledge with us and made us understand the real essence of the topic and created interest in us to work day

and night for the project; we also thank our project coordinator Mr.Mahesh, for his support and

encouragement.





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ABSTRACT:

Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in

crime. Our system can predict the type of crime activity which have high probability for given location interms

of latitude and longitude and date and also we can visualize crime prone areas. With the increasing advent of

computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of

solving crimes. Using the concept of data mining we can extract previously unknown, useful information from

an unstructured data. Here we have an approach between computer science and criminal justice to develop a

data mining procedure that can help solve crimes faster. Instead of focusing on causes of crime occurrence like

criminal background of offender, political enmity etc we are focusing mainly on crime factors of each day.

KEYWORDS: Clustering, k-means Algorithm, Decision Tree, Crime.

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CONTENTS

TITLE PAGE NO.

Abstract

Keywords

List of Figures

List of Tables

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5

vi

vii

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1. Introduction 9-10

1.1 Introduction 9

1.2 Problem Statement 10

2. Literature Survey 11-12

2.1.Cluster Aanlysis For Aanmoly

Detection In Accounting Data 11

2.2 Analysing Violent Criminal

Behaviour By Simulation Model 11

2.3 An Intelligent Analysis Of A City

Crime Data 12

3. Methodology 13-15

3.1

3.1.1 Clustering

3.1.2 Algorithm K-Means 13

3.1.3 Algorithm Illustration Process 13

3.2. Proposed System 14

3.2.1. Architecture 15

4. Design 16-22

4.1 UML Diagrams

4.1.1 Use case Diagram 17

4.1.2 Class Diagram 18

4.1.3 Sequence Diagram 19

4.1.4 Collaboration Diagram 20

4.1.5 Activity Diagram 21

4.1.6 Deployment diagram 22

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5. Experimental analysis and results 23-29

5.1 System configuration 23

5.1.1 Software requirements 23

5.1.2 Hardware requirements 23

5.2 Sample Code 24-29

5.3 Screen shots 30-38

5.4 Experimental Analysis/Testing 39-42

6. Conclusion and Future work 43-44

6.1 Conclusion 43

6.2 Future Work 44

7. References 45

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INTRODUCTION

1.1 INTRODUCION:

Day by day the crime rate is increasing considerably. Crime cannot be predicted since it is

neither systematic nor random. Also the modern technologies and hi-tech methods help criminals

in achieving their misdeeds. According to Crime Records Bureau crimes like burglary, arson etc

have been decreased while crimes like murder have been increased. Even though we cannot

predict who all may be the victims of crime but can predict the place that has probability for its

occurrence. The predicted results cannot be assured of 100% accuracy but the results shows that

our application helps in reducing crime rate to a certain extent by providing security in crime

sensitive areas. So for building such a powerful crime analytics tool we have to collect crime

records and evaluate it.

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1.2 PROBLEM STATEMENT:

Criminals are nuisance for the society in all corners of world for a long time now and

measures are required to eradicate crimes from our world. Our mission is to offer crime prevention

application to keep public safe. Current policing strategies work towards finding the criminals,

basically after the crime has occurred. But, with the help of technological advancement, we can

use historic crime data to recognize crime patterns and use these patterns to predict crimes

beforehand. We are using clustering algorithms to predict crime prone areas.

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2. LITERATURE SURVEY:

There are various papers which contributed to the study of sentimental classification of citations.

Based on the study of these papers, this project was proposed.

2.1CLUSTER ANALYSIS FOR ANAMOLY DETECTION IN ACCOUNTING DATA

Paper-1 Summary: Proposed by Sutapat Thirprungsri

The purpose of this study is to examine the possibility of using clustering technology for

continuous auditing. Automating fraud filtering can be of great value to preventive continuous

audits. In this paper, cluster-based outliers help auditors focus their efforts when evaluating group

life insurance claims. Claims with similar characteristics have been grouped together and those

clusters with small population have been flagged for further investigations. Some dominant

characteristics of those clusters are, for example, having large beneficiary payment, having huge

interest amount and having been submitted long time before getting paid. This study examines the

application of cluster analysis in accounting domain. The results provide a guideline and evidence

for the potential application of this technique in the field of audit.

2.2ANALYZING VIOLENT CRIMINAL BEHAVIOUR BY SIMULATION MODEL

Paper-2 Summary: Proposed by K. Zakhir Hussain

Crime analysis, a part of criminology, is a task that includes exploring and detecting crimes and

their relationships with criminals. The high volume of crime datasets and also the complexity of

relationships between these kinds of data have made criminology an appropriate field for applying

data mining techniques. Identifying crime characteristics is the first step for developing further

analysis. The knowledge that is gained from data mining approaches is a very useful tool which

can help and support in identifying violent criminal behaviour. The idea here is to try to capture

years of human experience into computer models via data mining and by designing a simulation

model.

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2.3 AN INTELLIGENT ANALYSIS OF A CITY CRIME DATA

Paper-3 Summary: There had been an enormous increase in the crime in the recent past. The

concern about national security has increased significantly since the 26/11 attacks at Mumbai,

India. However, information and technology overload hinders the effective analysis of criminal

and terrorist activities. Crime deterrence has become an upheaval task. The cops in their role to

catch criminals are required to remain convincingly ahead in the eternal race between law breakers

and law enforcers. Data mining applied in the context of law enforcement and intelligence

analysis holds the promise of alleviating such problem. In this paper we use a clustering/classify

based model to anticipate crime trends. The data mining techniques are used to analyse the city

crime data from Police Department.

2.4 DATA CRIME APPROACHES TO CRIMINAL CAREER ANALYSIS

Paper-4 Summary: Narrative reports and criminal records are stored digitally across individual

police departments, enabling the collection of this data to compile a nation-wide database of

criminals and the crimes they committed. The compilation of this data through the last years

presents new possibilities of analysing criminal activity through time. Augmenting the traditional,

more socially oriented, approach of behavioural study of these criminals and traditional statistics,

data mining methods like clustering and prediction enable police forces to get a clearer picture of

criminal careers.. Four important factors play a role in the analysis of criminal careers: crime

nature, frequency, duration and severity. This method yields a visual clustering of these criminal

careers and enables the identification of classes of criminals. The proposed method allows for

several user-denied parameters.

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3.METHODOLOGY:

3.1.1. CLUSTERING:

Clustering is an unsupervised task without having a priori knowledge by discovering

groups of similar documents. There are two types of categories in clustering algorithms; they are

the partitioned algorithm and the hierarchical algorithm. K-Means algorithm and the link

clustering they come under these two categories. K-Means and hierarchical clustering have many

comparisons. In hierarchical clustering the size of data increases as the computational expansive,

K-Means is faster. It updates the centroid clusters with each iteration and reallocates each

document by its nearest centroid by this we can say that it is an iterative algorithm

3.1.2 ALGORITHM K-MEANS:

K-means clustering is one of the method of cluster analysis which aims to partition n

observations into k clusters in which each observation belongs to the cluster with the nearest

mean. K means algorithm complexity is O(tcn), where n is instances, c is clusters, and t is

iterations and relatively efficient . It often terminates at a local optimum. Its disadvantage is

applicable only when mean is defined and need to specify c, the number of clusters, in advance. It

unable to handle noisy data and outliers and not suitable to discover clusters with non-convex

shapes.K-Means clustering investigation plans to partition n perceptions into k bunch during

which each perception includes a place with the bunch with the nearest centroid. 19

3.1.3 Algorithm Illustration Process:

1. Initially, the number of clusters must be known let it be k

2. The initial step is to choose a set of K instances as centres of the clusters.

3. Next, the algorithm considers each instance and assigns it to the cluster which is closest.

4. The cluster centroids are recalculated either after whole cycle of re-assignment or each instance

assignment.

5. This process is iterated.

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3.2 PROPOSED SYSTEM:

In the proposed system, we done crime data analysis of with many parameters and factors

including daily arrests, monthly arrests, number of domestic violence, top 5 monthly, weekly and

daily crime are visualized.

Using Decision Tree algorithm and K-means clustering algorithm, we are predicting the type of

crime for the given latitude and longitude.

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3.2.1ARCHITECTURE:

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4. DESIGN

4.1. UML DIAGRAMS:

UML DIAGRAMS

The design is a plan or drawing produced to show the look and function or workings of an

object before it is made. Unified Modeling language (UML) is a standardized modeling language

enabling developers to specify, visualize, construct and document artifacts of a software system.

Thus, UML makes these artifacts scalable, secure and robust in execution. UML is an important

aspect involved in object-oriented software development. It uses graphic notation to create visual

models of software systems.

The different types of UML diagram are as follows.

Use Case Diagram

Class Diagram

Activity Diagram

Sequence Diagram

Collaboration Diagram

Component Diagram

Deployment Diagram

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4.1.1 USE CASE DIAGRAM:

User

User

Figure: Use case Diagram

The above figure represent use case diagram of proposed system, where user inputs dataset,

we pre-process dataset, the algorithm Decision Tree and K-means clustering to generate the

trained model to predict the crime type. The actor and use case is represented. An eclipse

shape represents the use case namely input image, pre-process, Split features, prediction and

output.

Input Dataset

Pre-process

Split X,Y Train

Apply DT, K-means

Trained model

Test Input

Predict crime

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X_train;

Y_train

DecisionTreeRegressor()

Kmeans()

rf.fit()

dt.fit()

X_test;

Input;

DecisionTreeRegressor()

Kmeans()

rf.fit()

dt.fit()

Test

4.1.2 CLASS DIAGRAM

Figure: Class Diagram

The class diagram explains about the properties and functions of each class. The classes

are Main, pre-process, split data, train and test. In the above diagram, every class is represented

with attributes and operations.

Train_model

data_train():

data_test();

x=dataset[]

y=dataset[]

train_test_split(x, y)

Split_data

Main

from_date;

currentDate;

lat;

lon;

clear():

browse():

preprocess():

Kmeans():

decisiontree():

Pre-process

timestamp;

lat;

lon;

data_user1()

process(path):

pd.to_datetime():

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4.1.3 SEQUENCE DIAGRAM

Figure: Sequence Diagram

A sequence diagram shows a parallel vertical lines, different processes or objects that live

simultaneously, and as horizontal arrows, the messages exchanged between them, in order in

which they occur. The above figure represents sequence diagram, the proposed system’s sequence

of data flow is represented.

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4.1.4 COLLABORATION DIAGRAM

Figure: Collaboration Diagram

The above figure shows the collaboration diagram of the proposed system, where we represented the

collaboration between the actor and function modules with sequence number.

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4.1.5 ACTIVITY DIAGRAM

Figure: Activity Diagram

The above figure show the activity diagram of the proposed system, where we represented the

identified activities and its functional flow.

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4.1.6 DEPLOYMENT DIAGRAM

Figure: Deployment Diagram

In the deployment diagram the UML models the physical deployment of artifacts on nodes.

The nodes appear as boxes, and the artifacts allocated to each node appear as rectangles within the

boxes. Nodes may have subnodes, which appear as nested boxes. A single node in a deployment

diagram may conceptually represent multiple physical nodes, such as a cluster of database servers.

Client

Dataset

Middleware Pre-processprocess

Split X,Y

Train

Kmeans

DT

Server

Output

Trainedmodel

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5.EXPERIMENTNAL ANALYSIS AND RESULTS:

5.1SYSTEM CONFIGURATION:

The system requirements includes Hardware and Software requirement, which are provided below

5.1.1. Software Requirements

Operating System : Windows 7 or higher

Programming : Python 3.6 and related libraries

5.1.2Hardware Requirements

Processor : Any Processor above 500 MHz.

Ram : 4 GB

Hard Disk : 4 GB

Input device : Standard Keyboard and Mouse.

Output device : VGA and High Resolution Monitor.

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5.2SAMPLE CODE:

import csv

import numpy as np

import pandas as pd

from pandas import read_csv

import matplotlib.pyplot as plt

from pandas.plotting import scatter_matrix

import seaborn as sns

import matplotlib.pyplot as plt

sns.set(style="whitegrid")

def process(path):

print("preprocess")

df_main = pd.read_csv(path)

names=list(df_main.columns)

correlations = df_main.corr() #

plot correlation matrix

fig = plt.figure()

fig.canvas.set_window_title('Correlation Matrix') ax =

fig.add_subplot(111)

cax = ax.matshow(correlations, vmin=-1, vmax=1)

fig.colorbar(cax)

ticks = np.arange(0,9,1)

ax.set_xticks(ticks)

ax.set_yticks(ticks)

ax.set_xticklabels(names)

ax.set_yticklabels(names)

fig.savefig('results/Correlation Matrix.png')

plt.pause(5)

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plt.show(block=False)

plt.close()

crimes = read_csv(path, index_col='Date') s =

crimes[['PrimaryType']]

crimes.index = pd.to_datetime(crimes.index)

crime_count =

pd.DataFrame(s.groupby('PrimaryType').size().sort_values(ascending=False).rename('counts'

).reset_index())

# Initialize the matplotlib figure

f, ax = plt.subplots(figsize=(16, 15))

# Plot the total crashes

sns.set_color_codes("pastel")

sns.barplot(x="counts",y="PrimaryType",

data=crime_count.iloc[:10, :],label="Total", color="b")

ax.legend(ncol=2, loc="lower right", frameon=True)

ax.set(ylabel="Type",xlabel="Crimes")

sns.despine(left=True, bottom=True)

plt.savefig('results/Top10Crimes.png')

# Add a legend and informative axis label

plt.pause(10)


plt.close()

crimes_2015 = crimes.loc['2015']

## Yearly crimes

arrest_yearly = crimes[crimes['Arrest'] == True]['Arrest']

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plt.subplot()

# Monthly arrest

arrest_yearly.resample('M').sum().plot()

plt.title('Monthly arrests')

plt.savefig('results/Monthly arrests.png')

plt.pause(10)


plt.close()

# Weekly arrest

arrest_yearly.resample('W').sum().plot()

plt.title('Weekly arrests')

plt.savefig('results/Weekly arrests.png')

plt.pause(10)


plt.close()

# daily arrest

arrest_yearly.resample('D').sum().plot()

plt.title('Daily arrests')

plt.savefig('results/Daily arrests.png')

plt.pause(10)


plt.close()

domestic_yearly = crimes[crimes['Domestic'] == True]['Domestic']

plt.subplot()

# Monthly domestic violence

domestic_yearly.resample('M').sum().plot()

plt.title('Monthly domestic violence')

plt.savefig('results/Monthly domestic violence.png')

plt.pause(10)


plt.close()

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# Weekly domestic violence

domestic_yearly.resample('W').sum().plot()

plt.title('Weekly domestic violence')

plt.savefig('results/Weekly domestic violence.png')

plt.pause(10)


plt.close() # daily domestic violence

domestic_yearly.resample('D').sum().plot()

plt.title('Daily domestic violence')

plt.savefig('results/Daily domestic violence.png')

plt.pause(10)


plt.close()

theft_2015 =

pd.DataFrame(crimes_2015[crimes_2015['PrimaryType'].isin(['THEFT','BATTERY',

'CRIMINAL DAMAGE', 'NARCOTICS', 'ASSAULT'])]['PrimaryType'])

grouper_2015 = theft_2015.groupby([pd.TimeGrouper('M'), 'PrimaryType'])

data_2015 = grouper_2015['PrimaryType'].count().unstack()

data_2015.plot()

plt.title("Top 5 monthly crimes 2015")

plt.savefig('results/Top 5 monthly crimes 2015.png')

plt.pause(10)


plt.close()

grouper_2015 = theft_2015.groupby([pd.TimeGrouper('W'), 'PrimaryType'])


data_2015.plot()

plt.title("Top 5 Weekly crimes 2015")

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plt.savefig('results/Top 5 Weekly crimes 2015.png')

plt.pause(10)


plt.close()

grouper_2015 = theft_2015.groupby([pd.TimeGrouper('D'), 'PrimaryType'])


data_2015.plot()

plt.title("Top 5 daily crimes 2015")

plt.savefig('results/Top 5 daily crimes 2015.png')

plt.pause(10)


plt.close()

data = pd.read_csv(path,usecols=['Date', 'PrimaryType','Latitude','Longitude']) #

Preview the first 5 lines of the loaded data

data.dropna(inplace=True)

print(data)

print(data.head())

print(data.PrimaryType.unique())

data[["PrimaryType"]] = data[["PrimaryType"]].replace(['BATTERY','OTHER

OFFENSE','ROBBERY','NARCOTICS','CRIMINALDAMAGE','WEAPONS

VIOLATION','THEFT','BURGLARY','MOTOR VEHICLE THEFT','PUBLIC PEACE

VIOLATION','ASSAULT','CRIMINAL TRESPASS','CRIM SEXUAL

ASSAULT','INTERFERENCE WITH PUBLIC OFFICER','ARSON','DECEPTIVE

PRACTICE','LIQUOR LAW VIOLATION','KIDNAPPING','SEX OFFENSE','OFFENSE

INVOLVING

CHILDREN','PROSTITUTION','GAMBLING','INTIMIDATION','STALKING','OBSCENIT

Y','PUBLIC INDECENCY','HUMAN TRAFFICKING','CONCEALED CARRY LICENSE

VIOLATION','OTHER NARCOTIC VIOLATION','HOMICIDE','NON-CRIMINAL'],

[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30])

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print(data.PrimaryType.unique())

print(data)

data['year'] = pd.DatetimeIndex(data['Date']).year

data['month'] = pd.DatetimeIndex(data['Date']).month

data['day'] = pd.DatetimeIndex(data['Date']).day

data['hour'] = pd.DatetimeIndex(data['Date']).hour

data['min'] = pd.DatetimeIndex(data['Date']).minute

print(data)

data.to_csv("cleaned.csv")

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5.3 SCREENSHOTS:

The following screen represents the application home page

The following screen represents data visualization of correlation matrix

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The following screen represents the number of crimes in each category type

The following screen represents number of arrests for crime as monthly plot

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The following screen represents number of arrests for crime as weekly plot

The following screen represents number of arrests for crime as daily plot

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The following screen represents number of domestic violence as monthly plot

The following screen represents number of domestic violence as Weekly plot

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The following screen represents number of domestic violence as daily plot

The following screen represents top 5 monthly crimes

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The following screen represents top 5 weekly crimes

The following screen represents top 5 daily crimes

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The following screen shows the home page of application

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5.4 EXPERIMENTNAL TESTING

TESTING:

Introduction:

After finishing the development of any computer based system the next complicated time

consuming process is system testing. During the time of testing only the development company

can know that, how far the user requirements have been met out, and so on.

Software testing is an important element of the software quality assurance and represents

the ultimate review of specification, design and coding. The increasing feasibility of software as a

system and the cost associated with the software failures are motivated forces for well planned

through testing.

Testing Objectives

These are several rules that can save as testing objectives they are:

Testing is a process of executing program with the intent of finding an error.

A good test case is one that has a high probability of finding an undiscovered

error.

Testing procedures for the project is done in the following sequence

System testing is done for checking the server name of the machines being connected

between the customer and executive..

The product information provided by the company to the executive is tested against

the validation with the centralized data store.

System testing is also done for checking the executive availability to connected to the

server.

The server name authentication is checked and availability to the customer

Proper communication chat line viability is tested and made the chat system function

properly.

Mail functions are tested against the user concurrency and customer mail date

validate.

Following are the some of the testing methods applied to this effective project:

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1. SOURCE CODE TESTING:

This examines the logic of the system. If we are getting the output that is required by the

user, then we can say that the logic is perfect.

SPECIFICATION TESTING:

We can set with, what program should do and how it should perform under various

condition. This testing is a comparative study of evolution of system performance and system

requirements.

MODULE LEVEL TESTING:

In this the error will be found at each individual module, it encourages the programmer to

find and rectify the errors without affecting the other modules.

UNIT TESTING:

Unit testing focuses on verifying the effort on the smallest unit of software-module. The

local data structure is examined to ensure that the date stored temporarily maintains its integrity

during all steps in the algorithm’s execution. Boundary conditions are tested to ensure that the

module operates properly at boundaries established to limit or restrict processing.

INTEGRATION TESTING:

Data can be tested across an interface. One module can have an inadvertent, adverse effect

on the other. Integration testing is a systematic technique for constructing a program structure

while conducting tests to uncover errors associated with interring.

VALIDATION TESTING:

It begins after the integration testing is successfully assembled. Validation succeeds when

the software functions in a manner that can be reasonably accepted by the client. In this the

majority of the validation is done during the data entry operation where there is a maximum

possibility of entering wrong data. Other validation will be performed in all process where correct

details and data should be entered to get the required results.

RECOVERY TESTING:

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Recovery Testing is a system that forces the software to fail in variety of ways and

verifies that the recovery is properly performed. If recovery is automatic, re-initialization, and data

recovery are each evaluated for correctness.

SECURITY TESTING:

Security testing attempts to verify that protection mechanism built into system will in fact

protect it from improper penetration. The tester may attempt to acquire password through external

clerical means, may attack the system with custom software design to break down any defenses to

others, and may purposely cause errors.

PERFORMANCE TESTING:

Performance Testing is used to test runtime performance of software within the context of

an integrated system. Performance test are often coupled with stress testing and require both

software instrumentation.

BLACKBOX TESTING:

Black- box testing focuses on functional requirement of software. It enables to derive ets

of input conditions that will fully exercise all functional requirements for a program. Black box

testing attempts to find error in the following category:

Incorrect or missing function

Interface errors

Errors in data structures or external database access and performance errors.

OUTPUT TESTING:

After performing the validation testing, the next step is output testing of the proposed

system since no system would be termed as useful until it does produce the required output in the

specified format. Output format is considered in two ways, the screen format and the printer

format.

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USER ACCEPTANCE TESTING:

User Acceptance Testing is the key factor for the success of any system. The system under

consideration is tested for user acceptance by constantly keeping in touch with prospective system

users at the time of developing and making changes whenever required.

TEST CASES

Sl.

No

Test Case

Name

Test

Procedure

Pre-

Condition

Expected

Result

Passed/ failed

1 Data Input Enter no

details and

click submit

button

Enter no

details input

Alert “Select

Dataset, Enter

Latitude,

Longitude”

Passed

2 Data Input Select dataset

and click

submit button

Select dataset

and click

submit button

Alert “Select

Dataset, Enter

Latitude,

Longitude”

Passed

3 Data Input Select

dataset, enter

latitude and

click submit

button

Select

dataset, enter

latitude and

click submit

button

Alert “Select

Dataset, Enter

Latitude,

Longitude”

Passed

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6.CONCLUSION AND FUTURE WORK

6.1. CONCLUSION:

In this paper we have examined the accuracy of class and prediction based totally on different

check sets. Classification is done based on the Bayes theorem which showed more than 90%

accuracy. Using this algorithm we trained numerous news articles and build a model. For testing

we are inputting some test data into the model which shows better results. Our system takes

elements attributes of an area and preprocessing offers the frequent patterns of that place. The

pattern is used for constructing a model for decision tree. Corresponding to each place we build a

model by training on these frequent patterns. Crime patterns cannot be static since patterns change

over time. By training means we are teaching the system based on some particular inputs. So the

machine automatically learns the converting patterns in crime through examining the crime

patterns. Also the crime elements trade over time. By sifting through the crime data we have to

identify new factors that lead to crime. Since we are considering only some limited factors full

accuracy cannot be achieved. For getting better results in prediction we have to find more crime

attributes of places instead of fixing certain attributes. Till now we trained our system using

certain attributes but we are planning to include more factors to improve accuracy. Our

software predicts crime prone regions in India on a particular day. It will be more accurate if we

consider a particular state/region. Also another problem is that we are not predicting the time in

which the crime is happening. Since time is an important factor in crime we have to predict not

only the crime prone regions but also the proper time.

44

6.2. FUTURE SCOPE:

Experimental results prove that application is effective in terms of analysis speed, identifying

common crime patterns and crime prone areas for future prediction. From the encouraging results,

we believe that crime data mining has a promising future for increasing the effectiveness and

efficiency of criminal and intelligence analysis. Visual and intuitive criminal and intelligence

investigation techniques can be developed for crime pattern. As we have applied clustering

technique of data mining for crime analysis we can also perform other techniques of data mining

such as classification. Also we can perform analysis on various dataset such as enterprise survey

dataset, poverty dataset, aid effectiveness dataset, etc.

45

REFERENCES:

1. De Bruin ,J.S.,Cocx,T.K,Kosters,W.A.,Laros,J. and Kok,J.N(2006) Data mining

approaches to criminal carrer analysis ,”in Proceedings of the Sixth International

Conference on Data Mining (ICDM”06) ,Pp. 171-177.

2. Manish and M. P. GuptaGupta1, B.Chandra1 1,200Information System.

3. Nazlena Mohamad Ali1, Masnizah Mohd2, Hyowon Lee3, Alan F. Smeaton3, Fabio

Crestani4 and Shahrul Azman Mohd Noah2 ,2010 Visual Interactive Malaysia Crime

News Retrieval System 7 Crime Data Mining for Indian Police.

4. Chung-Hsien Yu, Max W.Ward, Melissa Morabito and Wei Ding,“Crime Forecasting

Using Data Mining Techniques”, 2011 11th IEEE International Conference on Data

Mining Workshops.

5. Tong Wang, Cynthia Rudin, Daniel Wagner, and Rich Sevieri. Detecting patterns of crime

with series finder. In Proceedings of the European Conference on Machine Learning

and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013),

2013.

6. Li Zhang, Yue Pan, and Tong Zhang. Focused named entity recognition using machine

learning. In Proceedings of the 27th Annual International.

7. Malathi. A and Dr. S. Santhosh Baboo. Article:an enhanced algorithm to predict a future

crime using data mining. International Journal of Computer Applications, 21(1):1–6, May

2011. Published by Foundation of Computer Science.

8. Eibe Frank and Remco R. Bouckaert. Naive bayes for text classification with unbalanced

classes. In Proceedings of the 10th European Conference on Principle and Practice of

Knowledge Discovery in Databases, PKDD’06, pages 503–510, Berlin, Heidelberg, 2006.

Springer-Verlag.

9. Wikipedia contributors.(9 July 2013 ), Stanford NLP. [Online].Available :http://www-

nlp.stanford.edu/software/dcoref.shtml. Last accessed: 24-Feb-2014, 10:00 AM.

10. Wikipedia contributors.(12 May 2014 at 19:05.), Series Finder.

[Online].Available:http://en.wikipedia.org/wiki/Crime_analysis, Last accessed: 12- Feb-

2014, 12:00 PM.

http://www-nlp.stanford.edu/software/dcoref.shtml



http://en.wikipedia.org/wiki/Crime_analysis

46

AN APPROACH FOR CRIME ANALYSIS USING K-MEANS CLUSTERING

ALGORITHM

1K. Suresh , 2K. Meghana Chowdary , 3P. Samyuktha , 4V. Ganesh Kumar , 5T. Y. Seshadhri

1Assistant Professor,2Scholar ,3 Scholar,4 Scholar,5Scholar 1Computer Science and Engineering.

1 Anil Neerukonda Institute of Technology&Sciences,Visakhapatnam,India.

ABSTRACT:

Crime analysis and prevention is a systematic approach for identifying and analyzing patterns

and trends in crime. Our system can predict the type of crime activity which have high probability

for given location in terms of latitude and longitude and date and also we can visualize crime prone

areas. With the increasing introduction of automated systems, crime statistics analysts can help the

Law enforcement officers to speed up the manner of fixing crimes. Using the idea of data mining we

will extract previously unknown, useful information from an unstructured records. Here we have an

method between laptop technology and crook justice to develop a information mining method that

can assist remedy crimes faster. Instead of focusing on reasons of crime incidence like criminal

history of offender, political enmity etc we are focusing particularly on crime elements of every day.

Keywords: Clustering, k-means Algorithm, Decision Tree, Crime.

1. INTRODUCTION:

In Today’s world crime rate is drastically increasing with increase in population. Crime

cannot be predicted since it is neither systematic nor random. Also the contemporary technology and

hi-tech methods assist criminals in reaching their misdeeds. According to Crime Records Bureau

crimes like burglary, arson etc has been decreased while crimes like homicide have been increased.

Even though we cannot predict who all may be the victims of crime but can predict the place that has

its occurrence. The predicted results cannot be assured of 100% accuracy but the results shows that

our application helps in reducing crime rate to a certain extent by providing security in crime

sensitive areas. So for building such a powerful crime analytical tool we have to collect crime

records and evaluate it.

1.1CRIME ANALYSIS:

Crime Analysis is an analytical process which gives related information about crime patterns

and trends in crime. Information on patterns helps the regulation enforcement organizations deploys

assets in more effective manner. Crime Analysis performs a important position in supplying

solutions to the crime troubles and formulating crime prevention strategies.

The main objectives of crime analysis are:

1. Detecting the crime type

2. Extracting the crime patterns by analyzing the crime and criminal data

3. predicting the crime based on the distribution of the existing criminal data and prediction of crime

rate using various data mining techniques.

47

2. SYSTEM ANALYSIS:

2.1 PROPOSED SYSTEM:

In the proposed system, we done crime data analysis of with many parameters and factors

including daily arrests, monthly arrests, number of domestic violence, top 5 monthly, weekly and

daily crimes are visualized.

Using Decision Tree algorithm and K-Means clustering algorithm, we are predicting the type

of crime for the given latitude and longitude.

2.2CLUSTERING:

Clustering is one of the data mining techniques which are used to place the data elements into

their related groups. It is the process of partitioning the data or objects into the same class. The data

which is present in the same class is more similar to each other to those in other cluster. The process

of partitioning the data objects into some subclasses is called a cluster. Clustering comes under

unsupervised learning. There are a variety of algorithms for clustering process, which generally share

the same property of interactively assigning records to a cluster.

K-means ALGORITHM:

The k-means algorithm assigns every factor to the cluster whose centroid is nearest.

Generally, the center is the common of all of the points in the cluster.

1. Choose the number of clusters, k.

2. Randomly find k clusters and determine the cluster centers, or directly generate k random points

as the cluster centers.

3. Assign each point to the nearest cluster center, where nearest is defined with respect to its

distance.

4. Recomputed the new cluster centres.

5. This process is iterated.

48

a. System Architecture

3.IMPLEMENTATION:

3.1. Data Collection: The data collection process involves the selection of quality data for analysis.

Here we used dataset with features namely latitude, longitude, and timestamp. The job of a data

analyst is to find ways and sources of collecting relevant and comprehensive data, interpreting it, and

analyzing results with the help of statistical techniques.

3.2. Data Visualization: A large amount of information represented in graphic form is easier to

understand and analyze. Some companies specify that a data analyst must know how to create slides,

diagrams, charts, and templates. In our approach, the data histogram and scatter matrix are shown as

data visualization part.

a.Data Visualization of Dataset- Correlation matrix b.Data Visualization by Crime category

49

c.Data Visualization by Top Monthly Crime types

Input Dataset: We gather the criminals data from their registered records and records are given to

the system. Here, we collect unstructured data. This unstructured data is stored in the database for

further purpose.

3.3. Pre-processing: Pre-processing is done with the data in the database. It is one of the data mining

techniques which is used to convert the raw data into a readable format. The Real-world data is often

incomplete, inconsistent and also contains many errors. So in this step, we remove those types of

errors in the data and make it for clustering which is used for prediction.

Clustering: Clustering is one of the data mining techniques which is used to place the data elements

into their related groups. It is the process of partitioning the data or objects into the same class. The

data which is present in the same class is more similar to each other to those in other cluster. The

process of partitioning the data objects into some subclasses is called a cluster. Clustering comes

under unsupervised learning. There are a variety of algorithms for clustering process, which

generally share the same property of interactively assigning records to a cluster.

3.4. Experimental Setup

Approach used:

K-Means Algorithm: K-Means Clustering Algorithm is one of the technique which is used for

portioning 'n' observations into 'k' clusters, in which each observation belongs to each cluster with

50

the nearest mean point.

Procedure:

1. Initialize the number of the clusters. Let the number of clusters be 'k'

2. In this step, choose the set of 'k' instances as the centers of the clusters.

3.The algorithm considers every instance and assigns those instances to the clusters which are

closest.

4. The cluster centroids are recalculated for the whole cycle and assigned.

5. This processed is iterated k times.

The complexity of the K-Means algorithm is O (tkn), where 'n' is the number of the instances;’t’ is

the iterations.

The disadvantage of this is it is applicable only when the mean value is specified and also need to

give ' c' i.e., number of clusters, in prior. It could not handle noisy data and does not suitable to

discover the clusters with non-convex shapes.

4. RESULTS:

a.Represents number of arrests for crime as monthly plot b.Represents number of arrests for crime as weekly plot

c.Represents daily domestic violence d.Represents top 5 crimes in a month

51

5. CONCLUSION:

In this paper we have examined the accuracy of class and prediction based totally on different check

sets. Classification is done based on the Bayes theorem which showed more than 90% accuracy.

Using this algorithm we trained numerous news articles and build a model. For testing we are

inputting some test data into the model which shows better results. Our system takes elements

attributes of an area and preprocessing offers the frequent patterns of that place. The pattern is used

for constructing a model for decision tree. Corresponding to each place we build a model by training

on these frequent patterns. Crime patterns cannot be static since patterns change over time. By

training means we are teaching the system based on some particular inputs. So the machine

automatically learns the converting patterns in crime through examining the crime patterns. Also the

crime elements trade over time. By sifting through the crime data we have to identify new factors

that lead to crime. Since we are considering only some limited factors full accuracy cannot be

achieved. For getting better results in prediction we have to find more crime attributes of places

instead of fixing certain attributes. Till now we trained our system using certain attributes but

we are planning to include more factors to improve accuracy. Our software predicts crime prone

regions in India on a particular day. It will be more accurate if we consider a particular state/region.

Also another problem is that we are not predicting the time in which the crime is happening. Since

time is an important factor in crime we have to predict not only the crime prone regions but also the

proper time.

6. REFERENCES:

1. De Bruin ,J.S.,Cocx,T.K,Kosters,W.A.,Laros,J. and Kok,J.N(2006) Data mining approaches to

criminal carrer analysis ,”in Proceedings of the Sixth International Conference on Data

Mining (ICDM”06) ,Pp. 171-177.

2. Manish and M. P. GuptaGupta1, B.Chandra1 1,200Information System.

3. Nazlena Mohamad Ali1, Masnizah Mohd2, Hyowon Lee3, Alan F. Smeaton3, Fabio Crestani4

and Shahrul Azman Mohd Noah2 ,2010 Visual Interactive Malaysia Crime News Retrieval

System 7 Crime Data Mining for Indian Police.

4. Chung-Hsien Yu, Max W.Ward, Melissa Morabito and Wei Ding,“Crime Forecasting Using

Data Mining Techniques”, 2011 11th IEEE International Conference on Data Mining

Workshops.

5. Tong Wang, Cynthia Rudin, Daniel Wagner, and Rich Sevieri. Detecting patterns of crime

with series finder. In Proceedings of the European Conference on Machine Learning

and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013),

2013.

6. Li Zhang, Yue Pan, and Tong Zhang. Focused named entity recognition using machine

learning. In Proceedings of the 27th Annual International.

7. Malathi. A and Dr. S. Santhosh Baboo. Article:an enhanced algorithm to predict a future

crime using data mining. International Journal of Computer Applications, 21(1):1–6, May

52

2011. Published by Foundation of Computer Science.

8. Eibe Frank and Remco R. Bouckaert. Naive bayes for text classification with unbalanced

classes. In Proceedings of the 10th European Conference on Principle and Practice of

Knowledge Discovery in Databases, PKDD’06, pages 503–510, Berlin, Heidelberg,

2006. Springer-Verlag.

9.Wikipedia contributors.(9 July 2013 ), Stanford NLP. [Online].Available :http://www-

nlp.stanford.edu/software/dcoref.shtml. Last accessed: 24-Feb-2014, 10:00 AM.

10. Wikipedia contributors.(12 May 2014 at 19:05.), Series Finder.

[Online].Available:http://en.wikipedia.org/wiki/Crime_analysis, Last accessed: 12-Feb-2014,

12:00 PM.



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