1 CITS3401:Data Warehousing and Data Mining Unit Coordinator: Wei Liu, email: [email protected]@uwa.edu.au Lecturer : Suprava Patnaik email: [email protected][email protected]

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CITS3401:Data Warehousingand Data Mining

Unit Coordinator: Wei Liu, email: [email protected]

Lecturer : Suprava Patnaik

email: [email protected]

Room 1.18 Computer Science & Software Engineering

Lecture hours: 9.00-11.00 am Thursday

Discussion slot: 1.30-2.30pm Thursday

Unit Web Page: http://undergraduate.csse.uwa.edu.au/units/CITS3401/

Lecture Slides, Project related information, Sample Question Patterns, Announcements and Help

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mailto:[email protected]

mailto:[email protected]

http://undergraduate.csse.uwa.edu.au/units/CITS3401/

http://undergraduate.csse.uwa.edu.au/units/CITS3401/

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CITS3401 Assessments

Two projects : 25% each An analysis of a business scenario through an OLAP tool.

We will be using an excel plug-in PALO for Data Warehousing Project.

An analysis of a data mining and exploration problem using WEKA.

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java Code

Labs will start from the fourth week. Both OLAP and WEKA can be downloaded freely

Final Examination: 50% Updates will be available in the course website

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CITS3401: Text Book

• Course Text Book: “Data Mining: Concepts and Techniques”, 2nd ed., Jiawei Han and Micheline Kamber- 2006

• .........., 3rd ed., Jiawei Han and Micheline Kamber, Jian Pei -2011

Jiawei Han‘s web page: http://web.engr.illinois.edu/~hanj/

References:• Data Mining: Methods and Techniques by, A.

Shawkat Ali and Saleh Wasimi Thomson, 2007

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http://web.engr.illinois.edu/~hanj/

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Text Book Screen Shut

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Chapter 1. Introduction

Why Data Mining?

What Is Data Mining? A Knowledge Discovery (KDD) Process

A Multi-Dimensional View of Data Mining/ classification What Kinds of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Kinds of Technologies Are Used?

What Kinds of Applications Are Targeted?

Are all the patterns interesting?

Integration of Data Mining System with Data Warehousing System

Major Issues in Data Mining

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Why Data Mining?

The Explosive Growth of Data: from terabytes to petabytes Data Explosion

Our capability of generating , collecting, storing and managing data has grown tremendously in the last 50 years.

Data collection and data availability Automated data collection tools, database systems, Web, computerized

society Major sources of abundant data

Business: Web, e-commerce, transactions, stocks, … Science: Remote sensing, bioinformatics, scientific simulation, … Society and everyone: news, digital cameras, YouTube

We are drowning in data, but starving for knowledge! “Necessity is the mother of invention”—Data mining—Automated

and scalable analysis of massive data sets7

Why Data Mining?—Potential Applications

Data analysis and decision support Market analysis and management

Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation

Risk analysis and management Forecasting, customer retention, improved underwriting,

quality control, competitive analysis Fraud detection and detection of unusual patterns (outliers)

Other Applications Text mining (news group, email, documents) and Web

mining Stream data mining

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Ex. 1: Market Analysis and Management

Where does the data come from?—Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies,

Target marketing Find clusters of “model” customers who share the same characteristics:

interest, income level, spending habits, etc. Determine customer purchasing patterns over time

Cross-market analysis—Find associations/co-relations between product sales, & predict based on such association

Customer profiling—What types of customers buy what products (clustering or classification)

Customer requirement analysis Identify the best products for different groups of customers Predict what factors will attract new customers

Provision of summary Information: Multidimensional summary reports Statistical summary information (data central tendency and variation)

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Ex. 2: Corporate Analysis & Risk Management

Finance planning and asset evaluation cash flow analysis and prediction contingent claim analysis to evaluate assets cross-sectional and time series analysis (financial-ratio,trend

analysis, etc.) Resource planning

summarize and compare the resources and spending Competition

monitor competitors and market directions group customers into classes and a class-based pricing

procedure set pricing strategy in a highly competitive market

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Ex. 3: Fraud Detection & Mining Unusual Patterns

Approaches: Clustering & model construction for frauds, outlier analysis

Applications: Health care, retail, credit card service, telecomm. Money laundering: suspicious monetary transactions Medical insurance:

Professional patients, ring of doctors, and ring of references Unnecessary or correlated screening tests

Telecommunications: phone-call fraud Phone call model: destination of the call, duration, time of day

or week. Analyze patterns that deviate from an expected norm Retail industry:

Analysts estimate that 38% of retail shrink is due to dishonest employees

Anti-terrorism:

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Evolution of Sciences

Before 1600, empirical science 1600-1950s, theoretical science

Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding.

1950s-1990s, computational science Over the last 50 years, most disciplines have grown a third, computational branch (e.g.

empirical, theoretical, and computational ecology, or physics, or linguistics.) Computational Science traditionally meant simulation. It grew out of our inability to find

closed-form solutions for complex mathematical models. 1990-now, data science

The flood of data from new scientific instruments and simulations The ability to economically store and manage petabytes of data online The Internet and computing Grid that makes all these archives universally accessible Scientific info. management, acquisition, organization, query, and visualization tasks scale

almost linearly with data volumes. Data mining is a major new challenge!

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Evolution of Database Technology

1960s: Data collection, database creation, IMS and network DBMS

1970s: Relational data model, relational DBMS implementation

1980s: RDBMS, advanced data models (extended-relational, OO, deductive, etc.) Application-oriented DBMS (spatial, scientific, engineering, etc.)

1990s: Data mining, data warehousing, multimedia databases, and Web databases

2000s Stream data management and mining Data mining and its applications Web technology (XML, data integration) and global information systems

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Why Data Mining?

Summary: Abundance of data and data archives are seldom

visited. Far exceeded human ability for comprehension Intuitive decisions are prone to biases and errors, and

is extremely time-consuming and costly Data mining tools perform data analysis and uncover

important data patterns, contributing greatly to business strategies, knowledge bases, and scientific and medical research.

Data Tombs

Nuggets of knowledge 14


Why Data Mining?

What Is Data Mining? A Knowledge Discovery from Data(KDD) Process








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What Is Data Mining?

Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously

unknown and potentially useful) patterns or knowledge from huge amount of data

Data mining: a misnomer? (Knowledge Mining from data) Alternative names

Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.

Watch out: Is everything “data mining”? Simple search and query processing (Deductive) expert systems

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What is Data Mining Tremendous amount of data (terabyte-petabyte)

High-dimensionality and high complexity of data Structured, un-structured, heterogeneous data

Scalable

Data mining involves integration of multiple disciplines: Machine learning Pattern recognition Statistics Databases Business Intelligence Big data

Efficient: Derived knowledge is new, interesting, informative and can be used for sophisticated application (decision making, process control, information management....)

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Data Mining: Confluence of Multiple Disciplines

Data Mining

Database Technology Statistics

MachineLearning

PatternRecognition

AlgorithmOther

Disciplines

Visualization

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Steps of Knowledge Discovery (KDD) Process

This is a view from typical database systems and data warehousing communities

Data mining plays an essential role in the knowledge discovery process

Data Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Selection

Data Mining

Pattern Evaluation

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Data Warehousing and Mining Framework

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KDD Process: Several Key Steps Learning the application domain

relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation

Find useful features, dimensionality/variable reduction, invariant representation

Choosing functions of data mining summarization, classification, regression, association,

clustering Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation

visualization, transformation, removing redundant patterns, etc.

Use of discovered knowledge21


Why Data Mining?









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Multi-Dimensional View of Data Mining

Data to be mined Database data (extended-relational, object-oriented, heterogeneous,

legacy), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks

Knowledge to be mined (or: Data mining functions) Characterization, discrimination, association, classification, clustering,

trend/deviation, outlier analysis, etc. Descriptive vs. predictive data mining Multiple/integrated functions and mining at multiple levels

Techniques utilized (methodologies) Data-intensive, data warehouse (OLAP), machine learning, statistics,

pattern recognition, visualization, high-performance, etc. Applications adapted

Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc. 23

Data Mining: On What Kinds of Data?

Structured and semi-structured data

Relational database/ Object-relational data

data warehouse,

transactional database

Unstructured data

Data streams and sensor data

Text data and web data

Time-series data, temporal data, sequence data (incl. bio-sequences)

Graphs, social networks and information networks

Spatial data, spatiotemporal data and multimedia data

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Relational Database

A relational database is a collection of tables, each of which is assigned a unique name.

Each table consists of a set of attributes (columns or fields) and usually stores a large set of tuples (records or rows).

Each tuple in a relational table represents an object identified by unique key and described by a set of attribute values.

A semantic data model, such as the entity relationship data model, is often constructed for relational databases.

An ER data model represents the database as a set of entities and their relationships.

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Relational Database

Relational data can be accessed by database queries written in a relational language such as SQL.

A given query is transformed into a set of relational operations such as join, selection and projection, and is then optimized for efficient processing.

Efficiency of retrieval, efficiency of update and integrity are the key requirements of a good relational database.

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An Example The relational database of the AllElectronics company has four

relational tables: customer, item, employee and branch. The relation customer consists of a set of attributes, including a

unique customer_ID, name, address, age, occupation, income, etc. Similarly, the other three relations have their attributes.

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Example of Queries

Show me a list of all items that were sold in the last quarter

Show me the total sales of the last month, grouped by branch

Which sales person has the highest amount of sales?

How many sales transactions occurred in the month of September?

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Purpose of relationaldatabases

The main purpose of a relational database is to store data correctly and retrieve data on demand.

This type of data processing is sometime called Online Transaction Processing (OLTP).

Relational databases are passive data repositories in the sense that a query only shows you what is stored in the database, but cannot tell you much about the meaning or trend of the data.

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Data Warehouse of AllElectronics

A data warehouse is a repository of information collected from multiple sources, stored under a unified schema, and that usually resides at a single site.

Need is to provide an analysis of the company’s sales per item type per branch for the a specified period.

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Data Warehouse the data warehouse may store a summary of the transactions per item type

for each store or, summarized to a higher level, for each sales region.

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Transactional Database

A transactional database consists of a file where each record represents a transaction.

Supports nested relation

Transaction id: Items, Customer name, date…

Sample Queries: Show me all the items purchased by ‘X’ How many transactions include item number ‘Y’? market basket data analysis: Which items sold well

together? (Frequent item set)

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Why Data Mining?









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Knowledge View: What Knowledge to be mined?

Data summary in multidimensional space Data cube and OLAP (On-Line Analytical Processing)

Pattern discovery Mining frequent patterns, association and correlation Applying pattern mining in many other tasks

Classification and predictive modelling Model construction based on some training examples Prediction of new data based on constructed models

Cluster analysis: How to group data to form new categories? Outlier analysis: Discovery of anomalies and rare events Trend and evolution analysis

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Data Mining Function: (1) Characterization and Discrimination

Data can be associated with classes or concepts. ( e.g., classes of items: computer, printers concept of customers: bigSpender, budgetSpender.. are the descriptions )

Multidimensional concept description: Characterization: summarizing the class in general. (e.g. general

specification of products whose sales increased by 10% and , ….profile of customers who spend more than $1000 a year. )

Discrimination: comparison of target class with a contrast class.( compare the two groups of customers, such as who shop computer products regularly versus who rarely shop such products). Drilling down on dimensions such as occupation, age,etc.)

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Data Mining Function: (2) Association and Correlation Analysis

Frequent patterns (or frequent item_sets) What items are frequently purchased together ?

Association, correlation vs. causality A typical association rule

Milk Bread [0.5%, 75%] (support, confidence) Are strongly associated items also strongly correlated?

How to mine such patterns and/or set rules efficiently in large datasets? ( single or multi-dimensional association, minimum support threshold)

How to use such patterns for classification, clustering, and other applications?

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Data Mining Function: (3) Classification

Classification and label prediction Construct models (functions) based on some training examples or rules….

[example: kind of response (good, mild, no) in sales campaign: price, brand, category, place_made…]

Describe and distinguish classes or concepts for future prediction E.g., classify countries based on (climate), or classify cars based on (gas

mileage) Predict some unknown class labels

Typical methods Decision trees, naïve Bayesian classification, support vector machines,

neural networks, rule-based classification, pattern-based classification, logistic regression, …

Typical applications: Credit card fraud detection, direct marketing, classifying stars, diseases,

web-pages, …37

Data Mining Function: (4) Cluster Analysis

Unsupervised learning (i.e., Class label is unknown) Group data to form new categories (i.e., clusters), e.g., cluster

houses to find distribution patterns Principle: Maximizing intra-class similarity & minimizing

interclass similarity Example: homogeneous sub-population of AllElectronics

customers (customer attributes: city, age, income,..) Many methods and applications

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Data Mining Function: (5) Outlier Analysis

Outlier analysis Outlier: A data object that does not comply with the general

behavior of the data Most data mining methods discard outliers as noise or

exceptions. Noise or exception? ― One person’s garbage could be

another person’s treasure Methods: by product of clustering or regression analysis,

distance analysis, statistical or probability model, Useful in fraud detection, rare events are more interesting Example: By detecting a purchase of extremely large amount

for a given account number.39

Time and Ordering: Sequential Pattern, Trend and Evolution Analysis

Sequence, trend and evolution analysis Trend, time-series, and deviation analysis: e.g., regression and

value prediction Sequential pattern mining

e.g., first buy digital camera, then buy large SD memory cards Periodicity analysis (e.g., overall stock market evolution

regularities or for particular companies) Motifs and biological sequence analysis

Approximate and consecutive motifs Similarity-based analysis

Mining data streams Ordered, time-varying, potentially infinite, data streams

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Structure and Network Analysis

Graph mining Finding frequent subgraphs (e.g., chemical compounds), trees (XML),

substructures (web fragments) Information network analysis

Social networks: actors (objects, nodes) and relationships (edges) e.g., author networks in CS, terrorist networks

Multiple heterogeneous networks A person could be multiple information networks: friends, family,

classmates, … Links carry a lot of semantic information: Link mining

Web mining Web is a big information network: from PageRank to Google Analysis of Web information networks

Web community discovery, opinion mining, usage mining, …

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Why Data Mining?









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Methodology View: Confluence of Multiple Disciplines

Data Mining

MachineLearning

Statistics

Applications

Algorithm

PatternRecognition

Distributed / cloud

computing

Visualization

Database Technology

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Why Confluence of Multiple Disciplines?

Tremendous amount of data Algorithms must be scalable to handle big data

High-dimensionality of data Micro-array may have tens of thousands of dimensions

High complexity of data Data streams and sensor data Time-series data, temporal data, sequence data Structure data, graphs, social and information networks Spatial, spatiotemporal, multimedia, text and Web data Software programs, scientific simulations

New and sophisticated applications

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Why Data Mining?









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Application View: Diverse Applications

Mining text data and mining the Web Web page classification and ranking, Weblog analysis,

recommender systems, … Mining business data

Transaction data, market basket analysis, fraud detection, … Data mining and software/system engineering e.g., mining

software bugs , optimize system performance, help in computer vision

Mining biological and medical data Gene, protein, microarray data, biological networks

Mining social and information networks Community discovery, information propagation, …

Invisible data mining : web search, stock market analysis

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Classification of Data Mining System

Classification according to the kinds of database mined: relational, transactional, ….spatial, text, stream data….or World Wide Web

Classification according to the kinds of knowledge mined: Based on mining functionalities, e.g. : characterization, discrimination, association, ….can be multiple and/or integrated data mining…., can be distinguished based on granularity…, regular or irregular patterns(outliers) mining

Classification according to the techniques utilized: degree of user interaction involved ( autonomous, interactive, query-driven), method of analysis (machine learning, pattern recognition, statistics, neural network….), combining merits of individual aspects..

Classification according to the applications adapted: Finance, Telecommunication, DNA, stock-market…all purpose data mining system may not fit for domain specific minig.

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Summary (till this)

Data mining: Discovering interesting patterns and knowledge from massive amount of data

A natural evolution of science and information technology, in great demand, with wide applications

A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation

Mining can be performed in a variety of data Data mining functionalities: characterization, discrimination, association,

classification, clustering, trend and outlier analysis, etc. Data mining technologies and applications

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Why Data Mining?


A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined?







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Evaluation of Knowledge

Are all mined knowledge interesting? One can mine tremendous amount of “patterns” Some may fit only certain dimension space (time, location,

…) Some may not be representative, may be transient, …

Evaluation of mined knowledge → directly mine only interesting knowledge? Descriptive vs. predictive Coverage Typicality vs. novelty Accuracy Timeliness …

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Are All the “Discovered” Patterns Interesting?

Data mining may generate thousands of patterns: Not all of them are

interesting Suggested approach: Human-centered, query-based, focused mining

Interestingness measures A pattern is interesting if it is easily understood by humans, valid on new or

test data with some degree of certainty, potentially useful, novel, or validates

some hypothesis that a user seeks to confirm

Objective vs. subjective interestingness measures Objective: based on statistics and structures of patterns, e.g., support,

confidence, etc.

Subjective: based on user’s belief in the data, e.g., unexpectedness, novelty,

actionability, etc.

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Find All and Only Interesting Patterns?

Find all the interesting patterns: Completeness Can a data mining system find all the interesting patterns? Do we need to

find all of the interesting patterns? Heuristic vs. exhaustive search Association vs. classification vs. clustering

Search for only interesting patterns: An optimization problem Can a data mining system find only the interesting patterns? Approaches

First general all the patterns and then filter out the uninteresting ones Generate only the interesting patterns—mining query optimization

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Integration of Data Mining and Data Warehousing

Data mining systems, DBMS, Data warehouse systems coupling

No coupling, loose-coupling, semi-tight-coupling, tight-coupling

On-line analytical mining data

integration of mining and OLAP technologies

Interactive mining multi-level knowledge

Necessity of mining knowledge and patterns at different levels of

abstraction by drilling/rolling, pivoting, slicing/dicing, etc.

Integration of multiple mining functions

Characterized classification, first clustering and then association

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Coupling Data Mining with DB/DW Systems

No coupling—flat file processing for developing efficient and effective algorithms,… is a poor design as may spend time in preprocessing.

Loose coupling- Fetching data from DB/DW. Mining does not explore data structure and optimization methods provided by DB & DW.Difficult for high scalability.

Semi-tight coupling—enhanced DM performance Provide efficient implement a few data mining primitives in a DB/DW

system, e.g., sorting, indexing, aggregation, histogram analysis, multiway join, precomputation of some statistical functions

Tight coupling—uniform processing environment DM is smoothly integrated into a DB/DW system, mining query is

optimized based on mining query, indexing, query processing methods, etc.

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Major Issues in Data Mining (1)

Mining Methodology Mining various and new kinds of knowledge Mining knowledge in multi-dimensional space at multiple level of

abstraction. Data mining: An interdisciplinary effort Boosting the power of discovery in a networked environment Handling noise, uncertainty, and incompleteness of data Pattern evaluation and pattern- or constraint-guided mining

User Interaction Interactive mining Background knowledge (integrity constraints & deduction rules) Presentation and visualization of data mining results

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Major Issues in Data Mining (2)

Efficiency and Scalability Efficiency and scalability of data mining algorithms Parallel, distributed, stream, and incremental mining methods

Diversity of data types Handling complex types of data Mining dynamic, networked, and global data repositories

Data mining and society Social impacts of data mining Privacy-preserving data mining Invisible data mining

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A Brief History of Data Mining Society

1989 IJCAI Workshop on Knowledge Discovery in Databases Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)

1991-1994 Workshops on Knowledge Discovery in Databases Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-

Shapiro, P. Smyth, and R. Uthurusamy, 1996) 1995-1998 International Conferences on Knowledge Discovery in Databases and Data

Mining (KDD’95-98) Journal of Data Mining and Knowledge Discovery (1997)

ACM SIGKDD conferences since 1998 and SIGKDD Explorations More conferences on data mining

PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), WSDM (2008), etc.

ACM Transactions on KDD (2007)

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Where to Find References? DBLP, CiteSeer, Google

Data mining and KDD (SIGKDD: CDROM) Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD

Database systems (SIGMOD: ACM SIGMOD Anthology—CD ROM) Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc.

AI & Machine Learning Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc. Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-PAMI,

etc. Web and IR

Conferences: SIGIR, WWW, CIKM, etc. Journals: WWW: Internet and Web Information Systems,

Statistics Conferences: Joint Stat. Meeting, etc. Journals: Annals of statistics, etc.

Visualization Conference proceedings: CHI, ACM-SIGGraph, etc. Journals: IEEE Trans. visualization and computer graphics, etc.

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Recommended Reference Books

E. Alpaydin. Introduction to Machine Learning, 2nd ed., MIT Press, 2011

S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000

T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003

U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining.

AAAI/MIT Press, 1996

U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann,

2001

J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. Morgan Kaufmann, 3 rd ed. , 2011

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2 nd ed.,

Springer, 2009

B. Liu, Web Data Mining, Springer 2006

T. M. Mitchell, Machine Learning, McGraw Hill, 1997

Y. Sun and J. Han, Mining Heterogeneous Information Networks, Morgan & Claypool, 2012

P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005

S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998

I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan

Kaufmann, 2nd ed. 2005

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Documents

1 CITS3401:Data Warehousing and Data Mining Unit Coordinator: Wei Liu, email: [email protected]@uwa.edu.au Lecturer : Suprava Patnaik email: [email protected][email protected]