Data Mining System and Applications: A Review

International Journal of Distributed and Parallel systems (IJDPS) Vol.1, No.1, September 2010
DOI : 10.5121/ijdps.2010.1103 32
DATA MINING SYSTEM AND
APPLICATIONS: A REVIEW
Mr. S. P. Deshpande1
and Dr. V. M. Thakare2
1
Department of MCA, D.C.P.E, H.V.P.Mandal Amravati, India
shrinivasdeshpande68@gmail.com
2
Post Graduate Deptt. of Computer Science,SGB, Amravati University, Amravati, India
vilthakare@yahoo.co.in
ABSTRACT:
In the Information Technology era information plays vital role in every sphere of the human life. It is very
important to gather data from different data sources, store and maintain the data, generate information,
generate knowledge and disseminate data, information and knowledge to every stakeholder. Due to vast
use of computers and electronics devices and tremendous growth in computing power and storage
capacity, there is explosive growth in data collection. The storing of the data in data warehouse enables
entire enterprise to access a reliable current database. To analyze this vast amount of data and drawing
fruitful conclusions and inferences it needs the special tools called data mining tools. This paper gives
overview of the data mining systems and some of its applications.
Keywords:
Data mining system architecture, Data mining application
1. INTRODUCTION
To generate information it requires massive collection of data. The data can be simple
numerical figures and text documents, to more complex information such as spatial data,
multimedia data, and hypertext documents. To take complete advantage of data; the data retrieval
is simply not enough, it requires a tool for automatic summarization of data, extraction of the
essence of information stored, and the discovery of patterns in raw data. With the enormous
amount of data stored in files, databases, and other repositories, it is increasingly important, to
develop powerful tool for analysis and interpretation of such data and for the extraction of
interesting knowledge that could help in decision-making. The only answer to all above is ‘Data
Mining’.
Data mining is the extraction of hidden predictive information from large databases; it is a
powerful technology with great potential to help organizations focus on the most important
information in their data warehouses [1,2,3,4]. Data mining tools predict future trends and
behaviors, helps organizations to make proactive knowledge-driven decisions [2]. The automated,
prospective analyses offered by data mining move beyond the analyses of past events provided by

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retrospective tools typical of decision support systems. Data mining tools can answer the
questions that traditionally were too time consuming to resolve. They prepare databases for
finding hidden patterns, finding predictive information that experts may miss because it lies
outside their expectations.
Data mining, popularly known as Knowledge Discovery in Databases (KDD), it is the
nontrivial extraction of implicit, previously unknown and potentially useful information from data
in databases[3,5]. Though, data mining and knowledge discovery in databases (or KDD) are
frequently treated as synonyms, data mining is actually part of the knowledge discovery
process[1,3,5].
2 THE DATA MINING TASKS:
The data mining tasks are of different types depending on the use of data mining result the
data mining tasks are classified as[1,2]:
1. Exploratory Data Analysis: It is simply exploring the data without any clear ideas of what we
are looking for. These techniques are interactive and visual.
2. Descriptive Modeling: It describe all the data, It includes models for overall probability
distribution of the data, partitioning of the p-dimensional space into groups and models describing
the relationships between the variables.
3. Predictive Modeling: This model permits the value of one variable to be predicted from the
known values of other variables.
4. Discovering Patterns and Rules: It concern with pattern detection, the aim is spotting
fraudulent behavior by detecting regions of the space defining the different types of transactions
where the data points significantly different from the rest.
5. Retrieval by Content: It is finding pattern similar to the pattern of interest in the data set.
This task is most commonly used for text and image data sets.
3. TYPES OF DATA MINING SYSTEMS:
Data mining systems can be categorized according to various criteria the
classification is as follows[3]:
• Classification of data mining systems according to the type of data source mined: This
classification is according to the type of data handled such as spatial data, multimedia
data, time-series data, text data, World Wide Web, etc.
• Classification of data mining systems according to the data model: This classification
based on the data model involved such as relational database, object-oriented database,
data warehouse, transactional database, etc.
• Classification of data mining systems according to the kind of knowledge discovered:
This classification based on the kind of knowledge discovered or data mining
functionalities, such as characterization, discrimination, association, classification,
clustering, etc. Some systems tend to be comprehensive systems offering several data
mining functionalities together.

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• Classification of data mining systems according to mining techniques used: This
classification is according to the data analysis approach used such as machine learning,
neural networks, genetic algorithms, statistics, visualization, database oriented or data
warehouse-oriented, etc.
The classification can also take into account the degree of user interaction involved in
the data mining process such as query-driven systems, interactive exploratory systems, or
autonomous systems. A comprehensive system would provide a wide variety of data
mining techniques to fit different situations and options, and offer different degrees of
user interaction.
4. DATA MINING LIFE CYCLE:
The life cycle of a data mining project consists of six phases[2,4]. The sequence of
the phases is not rigid. Moving back and forth between different phases is always
required. It depends on the outcome of each phase. The main phases are:
1. Business Understanding: This phase focuses on understanding the project objectives
and requirements from a business perspective, then converting this knowledge into a data
mining problem definition and a preliminary plan designed to achieve the objectives.
2 Data Understanding: It starts with an initial data collection, to get familiar with the
data, to identify data quality problems, to discover first insights into the data or to detect
interesting subsets to form hypotheses for hidden information.
3. Data Preparation: It covers all activities to construct the final dataset from the initial
raw data.
4. Modeling: In this phase, various modeling techniques are selected and applied and
their parameters are calibrated to optimal values.
5. Evaluation: In this stage the model is thoroughly evaluated and reviewed. The steps
executed to construct the model to be certain it properly achieves the business objectives.
At the end of this phase, a decision on the use of the data mining results should be
reached.
6. Deployment: The purpose of the model is to increase knowledge of the data, the
knowledge gained will need to be organized and presented in a way that the customer can
use it. The deployment phase can be as simple as generating a report or as complex as
implementing a repeatable data mining process across the enterprise.
5. THE DATA MINING MODELS:
The data mining models are of two types[1,2,6,45]: Predictive and Descriptive.
The predictive model makes prediction about unknown data values by using the known
values. Ex. Classification, Regression, Time series analysis, Prediction etc.
The descriptive model identifies the patterns or relationships in data and explores the
properties of the data examined. Ex. Clustering, Summarization, Association rule, Sequence
discovery etc.

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Many of the data mining applications are aimed to predict the future state of the data.
Prediction is the process of analyzing the current and past states of the attribute and prediction of
its future state. Classification is a technique of mapping the target data to the predefined groups or
classes, this is a supervise learning because the classes are predefined before the examination of
the target data. The regression involves the learning of function that map data item to real valued
prediction variable. In the time series analysis the value of an attribute is examined as it varies
over time. In time series analysis the distance measures are used to determine the similarity
between different time series, the structure of the line is examined to determine its behavior and
the historical time series plot is used to predict future values of the variable.
Clustering is similar to classification except that the groups are not predefined, but are
defined by the data alone. It is also referred to as unsupervised learning or segmentation. It is the
partitioning or segmentation of the data in to groups or clusters. The clusters are defined by
studying the behavior of the data by the domain experts. The term segmentation is used in very
specific context; it is a process of partitioning of database into disjoint grouping of similar tuples.
Summarization is the technique of presenting the summarize information from the data. The
association rule finds the association between the different attributes. Association rule mining is a
two-step process: Finding all frequent item sets, Generating strong association rules from the
frequent item sets. Sequence discovery is a process of finding the sequence patterns in data. This
sequence can be used to understand the trend.
6. THE KNOWLEDGE DISCOVERY PROCESS:
Data mining is one of the tasks in the process of knowledge discovery from the database. The
steps in the KDD process contains:[1,3]
1. Data cleaning: It is also known as data cleansing; in this phase noise data and irrelevant data
are removed from the collection.
2. Data integration: In this stage, multiple data sources, often heterogeneous, are combined in a
common source.
3. Data selection: The data relevant to the analysis is decided on and retrieved from the data
collection.
4. Data transformation: It is also known as data consolidation; in this phase the selected data is
transformed into forms appropriate for the mining procedure.
5. Data mining: It is the crucial step in which clever techniques are applied to extract potentially
useful patterns.
6. Pattern evaluation: In this step, interesting patterns representing knowledge are identified
based on given measures.
7. Knowledge representation: It is the final phase in which the discovered knowledge is visually
presented to the user. This essential step uses visualization techniques to help users understand
and interpret the data mining results.
7. DATA MINING METHODS:
The data mining methods are broadly categories as: On-Line Analytical Processing
(OLAP), Classification, Clustering, Association Rule Mining, Temporal Data Mining,
Time Series Analysis, Spatial Mining, Web Mining etc. These methods use different

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types of algorithms and data. The data source can be data warehouse, database, flat file or
text file. The algorithms may be Statistical Algorithms, Decision Tree based, Nearest
Neighbor, Neural Network based, Genetic Algorithms based, Ruled based, Support
Vector Machine etc. The selection of data mining algorithm is mainly depends on the
type of data used for mining and the expected outcome of the mining process. The
domain experts play a significant role in the selection of algorithm for data mining.
A knowledge discovery (KD) process involves preprocessing data, choosing a data-
mining algorithm, and post processing the mining results. There are very many choices
for each of these stages, and non-trivial interactions between them. Therefore both
novices and data-mining specialists need assistance in knowledge discovery processes.
The Intelligent Discovery Assistants [7] (IDA), helps users in applying valid
knowledge discovery processes. The IDA can provide users with three benefits:
1. A systematic enumeration of valid knowledge discovery processes;
2. Effective rankings of valid processes by different criteria, which help to choose
between the options;
3. An infrastructure for sharing knowledge, which leads to network externalities.
Several other attempts have been made to automate this process and design of a
generalized data mining tool that posses intelligence to select the data and data mining
algorithms and up to some extent the knowledge discovery.
8. DATA MINING APPLICATION:
The data mining applications can be generic or domain specific. The generic application is
required to be an intelligent system that by its own can takes certain decisions like: selection of
data, selection of data mining method, presentation and interpretation of the result. Some generic
data mining applications cannot take its own these decisions but guide users for selection of data,
selection of data mining method and for the interpretation of the results. The multi agent based
data mining application[8,10] has capability of automatic selection of data mining technique to be
applied. The Multi Agent System used at different levels[8]: First, at the level of concept
hierarchy definition then at the result level to present the best adapted decision to the user. This
decision is stored in knowledge Base to use in a later decision-making. Multi Agent System Tool
used for generic data mining system development[10] uses different agents to perform different
tasks.
A multi-tier data mining system is proposed to enhance the performance of the data mining
process[9]. It has basic components like user interface, data mining services, data access services
and the data. There are three different architectures presented for the data mining system namely
One-tire, Two-tire and Three-tire architecture.
Generic system required to integrates as many learning algorithms as possible and decides the
most appropriate algorithm to use. CORBA (Common Object Request Broker Architecture) has
features like:
Integration of different applications coded in any programming language considerably easy.
It allows reusability in a feasible way and finally it makes possible to build large and scalable
system.

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The data mining system architecture based on CORBA is given by Object Management
Group[10] has all characteristics to accomplish a distributed and object oriented computation.
A data-centric focus and automated methodologies makes data mining accessible to non-
experts[11]. The use of high-level interfaces can implement the automated methodologies that
hide the data mining concepts away from the users. A data-centric design hides away all the
details of mining methodology and exposes them through high-level tasks that are goal-oriented.
These goal-oriented tasks are implemented using data-centric APIs. This design makes data
mining task like other types of queries that users perform on the data.
In data mining better results could be obtained if large data is available. It leads to the
merging and linking of local databases. A new data-mining architecture based on Internet
technology addressed this problem.[12]
The context factor plays vital role in the success of data mining. The importance and meaning
of same data in the different context is different. A data in one context is very important may not
be much important in other context. A context-aware data-mining framework filters useful and
interesting context factors, and can produce accurate and precise prediction using those
factors[46].
The domain specific applications are focused to use the domain specific data and data mining
algorithm that targeted for specific objective. The applications studied in this context are aimed to
generate the specific knowledge. In the different domains the data generating sources generate
different type of data. Data can be from a simple text, numbers to more complex audio-video
data. To mine the patterns and thus knowledge from this data, different types of data mining
algorithms are used. The collection and selection of context specific data and applying the data
mining algorithm to generate the context specific knowledge is thus a skillful job. In many
domain specific data mining applications the domain experts plays vital role to mine useful
knowledge.
In the identification of foreign-accented French the audio files were used and the best 20 data
mining algorithms were applied[13] the Logistic Regression model found the most robust
algorithm than other algorithm.
In language research and language engineering many time extra linguistic information is
needed about a text. A linguistic profile that contains large number of linguistic features can be
generated from text file automatically using data mining[14]. This technique found quite effective
for authorship verification and recognition. A profiling system using combination of lexical and
syntactic features shows 97% accuracy in selecting correct author for the text. The linguistic
profiling of text effectively used to control the quality of language and for the automatic language
verification.[15] This method verifies automatically the text is of native quality. The results show
that language verification is indeed possible.
In medical science there is large scope for application of data mining. Diagnosis of diesis,
health care, patient profiling and history generation etc. are the few examples. Mammography is
the method used in breast cancer detection. Radiologists face lot of difficulties in detection of
tumors. Computer-aided methods could assist medical staff and improve the accuracy of
detection[16]. The neural networks with back-propagation and association rule mining used for
tumor classification in mammograms. The data mining effectively used in the diagnosis of lung
abnormality that may be cancerous or benign[17]. The data mining algorithms significantly
reduce patient’s risks and diagnosis costs. Using the prediction algorithms the observed
prediction accuracy was 100% for 91.3% cases. The use of data mining in health care is the
widely used application of data mining. The medical data is complex and difficult to analyze. A

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REMIND (Reliable Extraction and Meaningful Inference from Non-structured Data) system[21]
integrates the structured and unstructured clinical data in patient records to automatically create
high quality structured clinical data. The high quality of structuring allows existing patient
records to be mined to support guidelines compliance and to improve patient care.[21]
Data mining in distance learning automatically generate useful information to enhance the
learning process based on the vast amount of data generated by the tutors and student’s
interactions with web based distance-learning environment.[18] The Data Mining Applications
transfers the data into information and feedback to the e-learning environment. This solution
transforms large amounts of useless data into an intelligent monitoring and recommendation
system applied to the learning process.
In Web-based Education[42] the data mining methods are used to improve courseware. The
relationships are discovered among the usage data picked up during students’ sessions. This
knowledge is very useful for the teacher or the author of the course, who could decide what
modifications will be the most appropriate to improve the effectiveness of the course.
The data mining methods are also used to provide learners with real-time adaptive feedback
on the nature and patterns of their on-line communication while learning collaboratively[41]. This
makes it possible to increase the awareness of learners. The application of data mining methods
to educational chats is both feasible and can bring the improvement in learning environments.
Data mining facilitates software maintenance engineers to comprehend the structure of a
software system and assess its maintainability.[24] The clustering algorithm effectively used to
produce overviews of systems by creating mutually exclusive groups of classes, member data or
methods, according to their similarities and hence reduces the time required to understand the
overall system. This method also helps in discovering programming patterns and “unusual” or
outlier cases which may require attention.
The anomaly detection in the Network is very difficult and needs a very close watch on the
data traffic. The intrusion detection plays an essential role in computer security. The classification
method of data mining is used to classify the network traffic normal traffic or abnormal
traffic.[26]. If any TCP header does not belong to any of the existing TCP header clusters, then it
can be considered as anomaly.
A malicious executable is threat to system’s security, it damage a system or obtaining
sensitive information without the user’s permission. The data mining methods used to accurately
detect malicious executables before they run[25]. Classification algorithms RIPPER, Naive
Bayes, and a Multi-Classifier system are used to detect new malicious executables. This classifier
had shown detection rate 97.76%.
Sports are ideal for application of data mining tools and techniques. In the sports world the
vast amounts of statistics are collected for each player, team, game, and season. Data mining can
be used by sports organizations in the form of statistical analysis, pattern discovery, as well as
outcome prediction. Patterns in the data are often helpful in the forecast of future events. Data
mining can be used for scouting, prediction of performance, selection of players, coaching and
training and for the strategy planning[34]. The data mining techniques are used to determine the
best or the most optimal squad to represent a team in a team sport in a season, tour or game.[44]
The ‘Cy Young Award’[30] has been presented annually to the best pitcher in the major league of
baseball. The award is based largely on statistics compiled over the course of the baseball season.
A Bayesian classifier is developed to predict Cy Young Award winners in American major league
baseball.

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The Intelligence Agencies collect and analyze information to investigate terrorist activities.
One challenge to law enforcement and intelligent agencies is the difficulty of analyzing large
volume of data involve in criminal and terrorist activities. Data mining makes it easy, convenient
and practical to explore very large databases for organizations. The different data mining
techniques are used in crime data mining.[19,33,37,43] Entity extraction used to automatically
identify person, address, vehicle, narcotic drug, and personal properties from police narrative
reports. Clustering techniques used to automatically associate different objects such as persons,
organizations, vehicles etc. in crime records. Deviation detection is applied in fraud detection,
network intrusion detection, and other crime analyses that involve tracing abnormal activities.
Classification is used to detect email spamming and find authors who send out unsolicited emails.
String comparator is used to detect deceptive information in criminal record. Social network
analysis used to analyze criminals’ roles and associations among entities in a criminal network.
The data mining system implemented at the Internal Revenue Service to identify high-income
individuals engaged in abusive tax shelters[23] show significantly good results. The major lines
of investigation included visualization of the relationships and data mining to identify and rank
possibly abusive tax avoidance transactions.
Bankruptcy is the major threat to the banking sector[36] it increases the cost of lending. The
data mining algorithms effectively used for prediction of the personal bankruptcy. Predicting
bankruptcy has become the province of computer science rather than statistics. The data mining
method least squares regression; neural nets and decision trees are proved to be the suitable for
prediction of bankruptcy.
To enhance the quality of product data mining techniques can be used effectively. The data
mining technology SAS/EM is used to discover the rules those are unknown before and it can
improve the quality of products and decrease the cost. A regression model and the neural network
model when applied for this purpose given accuracy above 80%.[31] The neural network model
found better than the regression model.
E-commerce is also the most prospective domain for data mining[39]. It is ideal because
many of the ingredients required for successful data mining are easily available: data records are
plentiful, electronic collection provides reliable data, insight can easily be turned into action, and
return on investment can be measured. The integration of e-commerce and data mining
significantly improve the results and guide the users in generating knowledge and making correct
business decisions. This integration effectively solves several major problems associated with
horizontal data mining tools including the enormous effort required in pre-processing of the data
before it can be used for mining, and making the results of mining actionable.
The Digital Library retrieves, collects, stores and preserves the digital data. The advent of
electronic resources and their increased use in libraries has brought about significant changes in
Library[40]. The data and information are available in the different formats. These formats
include Text, Images, Video, Audio, Picture, Maps, etc. therefore digital library is a suitable
domain for application of data mining.
Retailers have been collecting large amount of data like customer information and related
transactions, product information etc. This significantly improves the applications like product
demand forecasting, assortment optimization, product recommendation and assortment
comparison across retailers and manufacturers[22]. To update the product details database is thus
the main issue. The text mining application for extraction of implicit attributes and explicit
attributes from product descriptions documents is the main task in such applications. Naive Bayes
and Expectation-Maximization these two methods of data mining are used in this context.

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In another application to design effective user interfaces for consumer information system
data mining can be used effectively[35]. Consumers use compensatory and non-compensatory
decision strategies when formulating their purchasing decisions. Compensatory decision-making
strategies are used when the consumer fully rationalizes their decision outcome whereas non-
compensatory decision-making strategies are used when the consumer considers only that
information which has most meaning to them at the time of decision. These decision-making
strategies are considered while designing online shopping support tools, and personalizing the
design of the user interface. The data mining methods cluster analysis and rough sets, are used to
obtain consumer information needed in support of designing customizable and personalized user
interface enhancements.
Group work has an important role in many aspects of life. This makes it important for people
to learn to be effective team members. Data mining is used for identifying patterns that
characterize successful groups from less successful ones[20]. The data mining algorithms are
used that can properly account for the temporal nature of the data and the character of group
interaction. There are two way processes involved, where theories of effective group behavior can
drive the data mining and, in the opposite direction, that the data mining should provide results
that are meaningful to groups wishing to improve their effectiveness. A frequent sequential
pattern-mining algorithm is used, which addresses the problem of discovering frequent sequences
in a database with a minimum frequency called support.
The data mining tools based on compositional analyses of the protein sequences are used to
analyze the genomes of representatives of three kingdoms of life, namely, archaea, eubacteria and
eukaryota.[27] The exploratory Principal Component Analysis carried out to classify the proteins
into clusters. Gene mapping is another application of data mining[29]. A large number of
computational approaches are used for data mining, and they tend to share certain attractive
characteristics for genetic association analysis. The data mining approaches mostly used are (1)
classification methods that directly aim to find markers and other features that help to predict the
disease status; (2) clustering techniques for finding subgroups of subjects, based on their
genotypic and phenotypic similarity, and analysis of their disease association; and (3) methods
based on the discovery of typical haploid types and analysis of their associations with the disease.
The Internet contains a large number of online documents available thus required an
automated text and document classification systems that are capable of automatically organizing
and classifying documents. There are several different data mining methods for text classification,
including statistical-based algorithms, Bayesian classification, distance-based algorithms, k-
nearest neighbors, decision tree-based methods etc.[28] Text classification techniques are used in
many applications on web, including e-mail filtering, mail routing, Spam filtering, news
monitoring, sorting through digitized paper archives, automated indexing of scientific articles,
classification of news stories and searching for interesting information on the WWW.
Data mining algorithms used effectively for classification of Arabic documents[28].
Developing text classification systems for Arabic documents is a challenging task due to the
complex and rich nature of the Arabic language. The N-gram frequency statistics technique
employing a dissimilarity measure called the “Manhattan distance”, and Dice’s measure of
similarity are used for classifying Arabic text documents.
The pharmaceutical industry is well known for performing quantitative analysis for clinical
research and market research[32]. In the marketing departments data mining applications are used
for sales force planning and direct marketing to doctors and consumers. Data mining techniques
used quite well to a variety of critical business decisions in the pharmaceutical industry. It also
used for forecasting production schedules for the manufacturing plants, determining market

41
potential in critical go/no decisions on continuing work on development compounds, or making
financial projections for stock holders and investors on Wall Street.
The prediction in engineering applications was treated effectively by a data mining
approach[17]. The prediction problems like the cost estimation problem in engineering, the
problem of engineering design that involves decisions where parameters, actions, components,
and so on are selected. This selection is often made based on prior data, information, or
knowledge. Numerous models and algorithms have been developed for autonomous predictions
based on data corresponding to different characteristics. The data mining algorithm applied on the
test file with nine features has produced 100% correct predictions. Several other applications
studied in this context.
9. CONCLUSION:
Most of the previous studies on data mining applications in various fields use the
variety of data types range from text to images and stores in variety of databases and data
structures. The different methods of data mining are used to extract the patterns and thus
the knowledge from this variety databases. Selection of data and methods for data mining
is an important task in this process and needs the knowledge of the domain. Several
attempts have been made to design and develop the generic data mining system but no
system found completely generic. Thus, for every domain the domain expert’s assistant is
mandatory. The domain experts shall be guided by the system to effectively apply their
knowledge for the use of data mining systems to generate required knowledge. The
domain experts are required to determine the variety of data that should be collected in
the specific problem domain, selection of specific data for data mining, cleaning and
transformation of data, extracting patterns for knowledge generation and finally
interpretation of the patterns and knowledge generation.
Most of the domain specific data mining applications show accuracy above 90%. The
generic data mining applications are having the limitations. From the study of various
data mining applications it is observed that, no application called generic application is
100 % generic. The intelligent interfaces and intelligent agents up to some extent make
the application generic but have limitations. The domain experts play important role in
the different stages of data mining. The decisions at different stages are influenced by the
factors like domain and data details, aim of the data mining, and the context parameters.
The domain specific applications are aimed to extract specific knowledge. The domain
experts by considering the user’s requirements, and other context parameters guide the
system. The results yield from the domain specific applications are more accurate and
useful. Therefore it is conclude that the domain specific applications are more specific for
data mining. From above study it seems very difficult to design and develop a data
mining system, which can work dynamically for any domain.
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