AUTOMATIC MINING FACETS FOR QUERIES FROM THEIR SEARCH RESULTS
USING DATA MINING ALGORITHM QUERYMINER
1 B. Suryanarayana Murthy, 2 Dr. V. Purnachandra Rao
1Research Scholar, Department of Computer Science, Dravidian University, Kuppam
2Professor of CSE/ IT, MLR Institute of Technology, Dundigal, Hyderabad.
Abstract:
Data mining has been used intensively and extensively by many organizations. In
healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data
mining applications can greatly benefit all parties involved in the healthcare industry. data
mining can help healthcare insurers detect fraud and abuse, healthcare organizations make
customer relationship management decisions, physicians identify effective treatments and best
practices, and patients receive better and more affordable healthcare services. The huge amounts
of data generated by healthcare transactions are too complex and voluminous to be processed and
analyzed by traditional methods. Data mining provides the methodology and technology to
transform these mounds of data into useful information for decision making. This manuscript
was proposing the algorithm for the healthcare system which is called query facets algorithm,
which can fetches data from the server based on query.
Keywords: Query Facet, Query Miner Algorithm, Healthcare system, headache, Data
Attributes, Simulation.
1 Introduction:
Data mining can be defined as the
process of finding previously unknown
patterns and trends in databases and using
that information to build predictive models.1
Alternatively, it can be defined as the
process of data selection and exploration
and building models using vast data stores
to uncover previously unknown patterns.[1]
Data mining is not new it has been used
intensively and extensively by financial
institutions, for credit scoring and fraud
detection; marketers, for direct marketing
and cross-selling or up-selling; retailers, for
market segmentation and store layout; and
manufacturers, for quality control and
maintenance scheduling. In healthcare, data
mining is becoming increasingly popular, if
not increasingly essential. Several factors
have motivated the use of data mining
applications in healthcare. The existence of
medical insurance fraud and abuse, for
example, has led many healthcare insurers to
attempt to [2] reduce their losses by using
data mining tools to help them find and
track offenders.[3] Fraud detection using
data mining applications is prevalent in the
commercial world, for example, in the
detection of fraudulent credit card
transactions. Recently, there have been
reports of successful data mining
applications in healthcare fraud and abuse
detection Another factor is that the huge
amounts of data generated by healthcare
transactions are too complex and
voluminous to be processed and analyzed by
traditional methods. Data mining can
improve decision-making by discovering
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patterns and trends in large amounts of
complex data.4 Such analysis has become
increasingly essential as financial pressures
have heightened the need for healthcare
organizations to make decisions based on
the analysis of clinical and financial data.
Insights gained from data mining can
influence cost, revenue, and operating
efficiency while maintaining a high level of
care.[4] Healthcare organizations that
perform data mining are better positioned to
meet their long-term needs
Yet another factor motivating the use of
data mining applications in healthcare is
the realization that data mining can
generate information that is very useful to
all parties involved in the healthcare
industry. For example, data mining
applications can help healthcare insurers
detect fraud and abuse, and healthcare
providers can gain assistance in making
decisions, for example, in customer
relationship management. Data mining
applications also can benefit healthcare
providers, such as hospitals, clinics and
physicians, and patients, for example, by
identifying effective treatments and best
practices There are also other factors
boosting data mining’s popularity. For
instance, as a result of the Balanced Budget
Act of 1997, the Centers for Medicare and
Medicaid Services must implement a
prospective payment system based on
classifying patients into case-mix groups,
using empirical evidence that resource use
within each case-mix group is relatively
constant. CMS has used data mining to
develop a prospective payment system for
inpatient rehabilitation.[5] The healthcare
industry can benefit greatly from data
mining applications. The objective of this
article is to explore relevant data mining
applications by first examining data
mining methodology and techniques; then,
classifying potential data mining
applications in healthcare; next giving an
illustration of a healthcare data mining
application; and finally, highlighting the
limitations of data mining and offering
some future directions.
1.1 Data Mining
History of Data Base and Data Mining
development and the history represented in
the Fig.1. The data mining system started
from the year of 1960s and earlier. In this,
the data mining is simply on file processing.
The next stage its Database management
Systems to be started year of 1970s early to
1980s. In this OLTP, Data modeling tools
and Query processing are worked. From
database management system there three
broad categories to be worked. First one is
Advanced Database Systems, this evaluated
year of Mid-1980s to present in this Data
models and Application oriented process are
worked.
Fig.1. History of Database Systems and
Data Mining
The Second part is Data Warehousing and
Data Mining worked since the year of the
late 1980s to present. The third part is Web
based Database Systems this started from
1990s to present and in this Web mining and
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XML based database systems are included.
These three broad categories are joined and
create the new process that’s called New
generation of the Integrated Information
system is started in 2000[6]
Data mining can be considered a relatively
recently developed methodology and
technology, coming into prominence only in
1994[7] It aims to identify valid, novel,
potentially useful, and understandable
correlations and patterns in data11 by
combing through copious data sets to sniff
out patterns that are too subtle or complex
for humans to detect.[8] Cross-Industry
Standard Process for Data Mining, or
CRISP-DM .Business understanding is
critical because it identifies the business
objectives and, thus, the success criteria of
data mining projects. Further, as the term
“data mining” implies, data is a crucial
component no data means no mining.
Hence, CRISP-DM includes data
understanding and data preparation in other
words, sampling and data transformation as
essential antecedents for modeling. The
modeling stage is the actual data analysis.
Most data mining software include online
analytical processing; traditional statistical
methods, such as cluster analysis,
discriminate analysis and regression
analysis; and non-traditional statistical
analysis, such as neural networks, decision
trees, link analysis and association analysis.
This extensive range of techniques is not
surprising in light of the fact that data
mining has been viewed as the offspring of
three different disciplines, namely database
management, statistics, and computer
science, including artificial intelligence and
machine learning. The evaluation stage
enables the comparison of models and
results from any data mining model by using
a common yardstick, such as lift charts,
profit charts, or diagnostic classification
charts. Data mining techniques can be
broadly classified based on what they can
do, namely description and visualization;
association and clustering; and classification
and estimation, which is predictive
modeling. Description and visualization can
contribute greatly towards understanding a
data set, especially a large one, and
detecting hidden patterns in data, especially
complicated data containing complex and
non-linear interactions. They are usually
performed before modeling is attempted and
represent data understanding in the CRISP-
DM methodology. In association, the
objective is to determine which variables go
together. For example, market-basket
analysis refers to a technique that generates
probabilistic statements such as, “If patients
undergo treatment A, there is a 0.35
probability that they will exhibit symptom
Z.” Such information can be useful for
investigating associative relationships in
healthcare. With clustering, the objective is
to group objects, such as patients, in such a
way that objects belonging to the same
cluster are similar and objects belonging to
different clusters are dissimilar. The most
common and important applications in data
mining probably involve predictive
modeling [9]. Classification refers to the
prediction of a target variable that is
categorical in nature, such as predicting
healthcare fraud vs. non fraud. Estimation,
on the other hand, refers to the prediction of
a target variable that is metric in nature,
such as predicting the length of stay or the
amount of resource utilization. For
predictive modeling, the data mining
techniques commonly used include
traditional statistics. They also include non-
traditional methods developed in the areas of
artificial intelligence and machine learning.
The two most important models of these are
neural networks and decision trees.
1.2 Techniques of Data Mining
There are many data mining techniques and
algorithms are available to discover
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meaningful pattern and rules [10].There are
many different techniques are as follow:
Classification: In classification, first
examine the features of newly presented
object and assign it to a predefined class for
example classify the credit applicants as
low, medium or high risk.[10]
Association: The main goal of association is
to establish the relationship between items
which exist in the market. The typical
examples of association modeling are
Market basket Analysis and cross selling
programs. The tools used for association
rule mining are apriori algorithm and weka
tool kit. [11]
Prediction: In this functionality, prediction
of some unknown or missing attributes
values based on other Information. For
example: Forecast the sale value for next
week based on available data.[12,13]
Clustering: In this, Data Mining organizes
data into meaningful sub-groups (clusters)
such that points within the group are similar
to each other, and as different as possible
from the points in the other groups. It is an
unsupervised classification. An effective
dynamic unsupervised clustering
algorithmic approach for market basket
analysis has been proposed by Verma et
al.[14].
Outlier Analysis: In this, Data Mining is
done to identify and explain exceptions. For
example, in case of Market Basket Data
Analysis, outlier can be some transaction
which happens unusually. [15]
2 Data Mining Application Areas
Data mining is driven in part by new
applications which require new capabilities
that are not currently being supplied by
today’s technology. These new applications
can be naturally into two broad categories
[16].
2.1 Applications:
Business and E-Commerce.
Scientific, Engineering and Health
Care Data
2.2 Data Mining Tasks:
Data mining tasks are mainly classified into
two broad categories they are, Predictive
model Descriptive model which was given
in the Fig .2.
Fig .2. Data mining models and tasks
2.3 Data Mining Applications In
Healthcare Sector
Healthcare industry today generates large
amounts of complex data about patients,
hospital resources, disease diagnosis,
electronic patient records, medical devices
etc. Larger amounts of data are a key
resource to be processed and analyzed for
knowledge extraction that enables support
for cost-savings and decision making. Data
mining applications in healthcare can be
grouped as the evaluation into broad
categories [17,18],
2.4 Treatment effectiveness
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Data mining applications can develop to
evaluate the effectiveness of medical
treatments. Data mining can deliver an
analysis of which course of action proves
effective by comparing and contrasting
causes, symptoms, and courses of
treatments.
2.5 Healthcare management
Data mining applications can be
developed to better identify and track
chronic disease states and high-risk patients,
design appropriate interventions, and reduce
the number of hospital admissions and
claims to aid healthcare management. Data
mining used to analyze massive volumes of
data and statistics to search for patterns that
might indicate an attack by bio-terrorists.
2.6 Customer relationship management
Customer relationship management is a
core approach to managing interactions
between commercial organizations-typically
banks and retailers-and their customers, it is
no less important in a healthcare context.
Customer interactions may occur through
call centers, physicians’ offices, billing
departments, inpatient settings, and
ambulatory care settings
2.7 Fraud and abuse
Detect fraud and abuses establish
norms and then identify unusual or abnormal
patterns of claims by physicians, clinics, or
others attempt in data mining applications.
Data mining applications fraud and abuse
applications can highlight inappropriate
prescriptions or referrals and fraudulent
insurance and medical claims.
2.8 Medical Device Industry
Healthcare system’s one important
point is medical device. For best
communication work this one is mostly
used. Mobile communications and low-cost
of wireless bio-sensors have paved the way
for development of mobile healthcare
applications that supply a convenient, safe
and constant way of monitoring of vital
signs of patients [19]. Ubiquitous Data
Stream Mining (UDM) techniques such as
light weight, one-pass data stream mining
algorithms can perform real-time analysis
on-board small/mobile devices while
considering available resources such as
battery charge and available memory.
2.9 Pharmaceutical Industry
The technology is being used to help
the pharmaceutical firms manage their
inventories and to develop new product and
services. A deep understanding of the
knowledge hidden in the Parma data is vital
to a firm’s competitive position and
organizational decision-making.
2.10 Hospital Management
Organizations including modern
hospitals are capable of generating and
collecting a huge amount of data.
Application of data mining to data stored in
a hospital information system in which
temporal behavior of global hospital
activities is visualized [20]. Three layers of
hospital management, Services for hospital
management, Services for medical staff,
Services for patients.
The following chart Fig. 3 , based on
Deloitte’s findings, shows how many
Smartphone users are aware of and/or
already using different AI-powered
applications on their devices, and it explains
that how many user have good awareness
about the data searching, which data is
useful for their regular usage in the daily
life.
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Fig. 3: Users awareness on data
searching in the daily life [21].
3 Previous work
Facet mining technique can be used for
different kinds of applications. This
technique is used for huge library database
applications and information science
research applications and to some computer
science research applications and
commercial search applications
Eg.Amazon.com need facets mining
application in order to get required data in
efficient manner in Web Queries data
mining.[22].
Different from query facet mining that
generates facets for each query without any
domain assumptions or prior knowledge,
some traditional faceted search approaches
are mainly built on a specific domain or
predefined facet categories. The problem of
automatically mining facet metadata and
mapping documents onto these categories
has been studied for years [23], A robust
review of faceted search is beyond the scope
of this paper. Dakka et al. [24, 25, 26]
developed methods to extract facet
hierarchies for a text corpus or a text
database then assign each document to those
facets. One main difference is that we aim to
mine several semantically coordinate lists of
items to guide users’ search, while Dakka’s
methods focus on building concept
hierarchies. E. Stoica et al. [27] proposed
Castanet to automatically generate domain-
specific faceted metadata from textual
descriptions of items based on existing
external lexical database Word Net.
FaSet model proposed by Bonino et al. [28]
focuses on using structured data in relational
database. It provides the formal definitions
of facet, facet space, focus, multi-
dimensional classification, etc. In addition
FaSet offers two search algorithms for
faceted navigation and search results
ranking respectively, which are
implemented in SQL.
Li et al. [29] presented a facet model with a
Directed Acyclic Graph (DAG) structure
based on the set theory. It is used in
Faceted-pedia system. This model gives the
formal definitions of category hierarchy,
facet, navigation path, faceted interface, and
provides the facet ranking operation based
on the navigation cost and pair wise
similarity among facets. facet model. FKR
model gives the formal definitions of unit,
relation, facet, interpretation, and organizes
facet terms into a lattice structure. In
contrast to the above set theory based
models, FKR can only map data items to a
single taxonomy and has no interactivity.
Faceted Lightweight Ontology proposed by
Giunchiglia et al. [30] is a typical
lightweight ontology. It has a rooted tree
structure where each node is associated with
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a natural language label. The labels of nodes
are organized according to certain
predefined patterns that capture different
aspects of items. This model gives the
definitions of category ontology, lightweight
ontology, and faceted lightweight ontology,
but does not provide interactive operations.
Organizing information into a hierarchical
category structure to facilitate information
seeking is a long standing research topic
[31]. Findings from eye tracking studies and
transaction log analysis demonstrated that a
substantial portion of users’ time was spent
on interactions with the result categories
[32]. On average, a user spent around 25 s
looking at the facets and 50 s looking at the
search results for an individual search.
According to a study by Kules and
Shneiderman [34]. Their findings showed
that the categorization offered an overview
of the result list and encouraged users to
explore more deeply and broadly. Users
were able to better assess search results and
felt more organized [34]. In health
information retrieval, Pratt [35] suggested
organizing results into categories based on
the United Medical Language System
(UMLS). Similar documents were
dynamically classified into groups in
accordance with a predefined terminology
model and a query model. The terminology
model consisted of the Medical Subjects
Headings (MeSH) and the semantic types in
the UMLS Semantic Network. The query
model provided information about the types
of the queries [35]. Based on these models, a
search system called DynaCat was
developed and its effectiveness was
evaluated in a user study. Fifteen patients
with breast cancer and their family members
participated in the study. The results showed
that, as compared to the traditional
document ranking list tool, users found
significantly more answers in a fixed
amount of time and felt more satisfied when
using DynaCat [36]. Later studies on
PubMed using MeSH browser [37] and a
subset of MEDLINE database using UMLS
Metathesaurus [38] also concluded the
helpfulness of facet category in users’ health
and biomedical information retrieval. For
exploratory search, it is important for the
search system to effectively support users’
navigation of the vast amount of retrieved
medical citations [39,40]
4 Proposed work
Data mining is an essential step of
knowledge discovery. In recent years it has
attracted great deal of interest in Information
industry [41]. Knowledge discovery process
consists of an iterative sequence of data
cleaning, data integration, data selection,
data mining pattern recognition and
knowledge presentation. In particulars, data
mining may accomplish class description,
association, classification, clustering,
prediction and time series analysis. Data
mining in contrast to traditional data
analysis is discovery driven [42].
In nowadays so many persons like illiterate
and literates suffering from Headache it is
not a small issue, it is growing up day by
day to generate a decease .So many types of
Headache here how to resolve problem and
some suggestions giving in result .The
different stages in the headache, and its
related suggestions like medication, doctors
suggestions their address and fee details has
discussed in the manuscript has given in this
result. The completed result was simulated
by the local database and result was satisfied
by the expected result.
Query Faceted Search
Faceted search is a technique for accessing
information organized according to a faceted
classification system, allowing users to
digest, analyze and navigate through
multidimensional data. It is widely used in
e-commerce and digital libraries [43].
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Faceted search is similar to query facet
extraction in that both of them use sets of
coordinate terms to represent different facets
of a query. However, most existing works
for faceted search are build on as specific
domain or predefined categories [44], while
query facet extraction does not restrict
queries in a specific domain, like products,
people, etc.
Query facet extraction is the problem of
finding query facets for a given query q
from available resources, such as web search
results. A query facet F t is a set of
coordinate terms, terms that are part of a
semantic set, which we call facet terms.
Query facets can be extracted from a variety
of different resources, such as a query log,
anchor text, taxonomy and social
folksonomy. In this work, we only focus on
extracting query facets from the top k web
search results 1 2 3, , ......, KD D D D D .We
intend to explore the use of other
information sources for this problem in
future work.
QDMiner extracts lists from free text,
HTML tags, and repeat regions contained in
the top search results, groups them into
clusters based on the items they contain,
then ranks the clusters and items based on
how the lists and items appear in the top
results. To summarize the information
contained in the query to find a list of
related queries. QDMiner, to automatically
mine query aspects by way of extracting and
grouping common lists from loose textual
content.
Algorithms
DDAL (Deduplication algorithm) algorithm
is proposed to detect and remove the
duplicate facets or items.
Architecture
The data backup stream from the computing
device is sent to the application aware
module in which chunks the data stream is
chunked into the various chunks depending
upon the type of the data that it belongs to.
Then the files are taken to similarity
function depending on the application or the
type of the file. Then the corresponding
similarity function is applied to the files. If
the similarity of the files defines to be
duplicate of the file already present in the
cloud backup, then the data will generated as
pairs and sent to the genetic programming
module for deduplication process.
5 Results & Discussion:
As per Cephalalgia study, The International
survey for Headache, one thousand two
hundred and seven migraine patients were
tested at The Headache Centre of Atlanta, a
clinical practice in the USA. The graph in
Fig.4. Shows the results of this study [45]
Fig.4. Headache triggers Vs people’s
percentage.
0
10
20
30
40
50
60
70
80
Stre
ss
Ho
rmo
ne
No
t ea
tin
g
We
ath
er
Sle
ep d
istu
rban
ces
Pe
rfu
me
/od
ou
r
Nec
k p
ain
Ligh
ts
Alc
oh
ol
Smo
ke
Sle
epin
g la
te
Hea
t
Foo
d
Headache Triggers in
percentage
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When a faceted search system processes a
query, its first task is to determine the set of
documents that satisfy the query constraints.
This set retrieval is straightforward and can
be accomplished efficiently using standard
inverted index techniques, such as those
described in [46]. The subsequent task,
however, is to show the facet values
available for refining the set of results. In
addition, faceted search applications often
show the counts associated with these
refinements.
The task of computing refinements is
significantly more demanding than that of
computing the set of results. One of the
health issue headache was taken as an
example to test the algorithm. The query
faceted result was simulated In ‘C’
language.
The complete result was displayed in the
terminal based on the query. The different
Types of Data Attributes, based on the
Query have given in the listed figures. Here
the result is explaining about the types of
headaches, reason for headache, and it is
suggesting about the medication, Doctors
address and fee details.
The Data Attributes has created in the Excel
which is interfaced with the local database.
When one data attribute is activated
automatically the related query will raise,
based on the attribute selection it will
fetches data or sub linked Data Attribute
from the local server.
Fig.5. Home button.
Fig .6. Types of Headaches.
Fig .7. Primary for Headaches.
Fig .8. Secondary headaches.
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Table.1. Medication for the headaches.
Define Symptoms Preventive Medication Progression
Migraine is an intense
pulsing from deep within
your head. This pain can
last for days. The
headache significantly
limits your ability to carry
out your daily routine.
Migraine is throbbing and
usually one-sided. People
with migraine headaches
are often sensitive to light
and sound. Nausea and
vomiting also usually
occur.
These Symptoms
before the headache
1.Starts flashing lights
2.Shimmering lights
3.Zigzag lines
4.Stars
5.Blind spots
1.Sumatriptan
2.Rizatriptan
3.Propranolo
4.Metoprolol 5.Topiramate 6n.Amitriptyline
1.Stroke,
2.Heart Disease,
3.High B.P,
4.Hearing
Problems,
5. Posttraumatic
Stress Disorder.
The simulated experimental result was
depicted in the Fig.5, Fig.6, Fig.7, Fig.8,
and the suggestible medication has given in
the Table.1.
Conclusion:
It can be concluded from the present
research that headache was the common
thing for many persons, which can happen in
different ways for all aged persons. The
research proposed methodology gives the
information about causes for the headache; it
can also give the brief information about the
medication for the headache and the details
of the doctor like fee structure, location of
the treatment.
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