data mining and the purpose of using mining

May 16, 2025 Data Mining: Concepts and Techniqu 1
Data Mining:
Concepts and
Techniques
— Slides for Textbook —
— Chapter 10 —
©Jiawei Han and Micheline Kamber
Intelligent Database Systems Research Lab
School of Computing Science
Simon Fraser University, Canada
http://www.cs.sfu.ca

Chapter 10: Applications and
Trends in Data Mining
 Data mining applications
 Data mining system products and research
prototypes
 Additional themes on data mining
 Social impact of data mining
 Trends in data mining
 Summary

Data Mining Applications
 Data mining is a young discipline with wide and
diverse applications

There is still a nontrivial gap between general
principles of data mining and domain-specific,
effective data mining tools for particular
applications
 Some application domains (covered in this chapter)
 Biomedical and DNA data analysis
 Financial data analysis
 Retail industry
 Telecommunication industry

Biomedical Data Mining and
DNA Analysis
 DNA sequences: 4 basic building blocks (nucleotides):
adenine (A), cytosine (C), guanine (G), and thymine (T).
 Gene: a sequence of hundreds of individual nucleotides
arranged in a particular order
 Humans have around 100,000 genes
 Tremendous number of ways that the nucleotides can
be ordered and sequenced to form distinct genes
 Semantic integration of heterogeneous, distributed
genome databases
 Current: highly distributed, uncontrolled generation
and use of a wide variety of DNA data
 Data cleaning and data integration methods
developed in data mining will help

DNA Analysis: Examples
 Similarity search and comparison among DNA sequences
 Compare the frequently occurring patterns of each class (e.g.,
diseased and healthy)
 Identify gene sequence patterns that play roles in various diseases
 Association analysis: identification of co-occurring gene sequences
 Most diseases are not triggered by a single gene but by a
combination of genes acting together
 Association analysis may help determine the kinds of genes that
are likely to co-occur together in target samples
 Path analysis: linking genes to different disease development stages
 Different genes may become active at different stages of the
disease
 Develop pharmaceutical interventions that target the different
stages separately
 Visualization tools and genetic data analysis

Data Mining for Financial Data Analysis
 Financial data collected in banks and financial institutions
are often relatively complete, reliable, and of high quality
 Design and construction of data warehouses for
multidimensional data analysis and data mining
 View the debt and revenue changes by month, by
region, by sector, and by other factors
 Access statistical information such as max, min, total,
average, trend, etc.
 Loan payment prediction/consumer credit policy analysis
 feature selection and attribute relevance ranking
 Loan payment performance
 Consumer credit rating

Financial Data Mining
 Classification and clustering of customers for
targeted marketing
 multidimensional segmentation by nearest-
neighbor, classification, decision trees, etc. to
identify customer groups or associate a new
customer to an appropriate customer group
 Detection of money laundering and other financial
crimes
 integration of from multiple DBs (e.g., bank
transactions, federal/state crime history DBs)
 Tools: data visualization, linkage analysis,
classification, clustering tools, outlier analysis, and
sequential pattern analysis tools (find unusual
access sequences)

Data Mining for Retail Industry
 Retail industry: huge amounts of data on sales,
customer shopping history, etc.
 Applications of retail data mining

Identify customer buying behaviors

Discover customer shopping patterns and trends

Improve the quality of customer service

Achieve better customer retention and satisfaction

Enhance goods consumption ratios

Design more effective goods transportation and
distribution policies

Data Mining in Retail Industry:
Examples
 Design and construction of data warehouses based on
the benefits of data mining
 Multidimensional analysis of sales, customers,
products, time, and region
 Analysis of the effectiveness of sales campaigns
 Customer retention: Analysis of customer loyalty
 Use customer loyalty card information to register
sequences of purchases of particular customers
 Use sequential pattern mining to investigate changes
in customer consumption or loyalty
 Suggest adjustments on the pricing and variety of
goods
 Purchase recommendation and cross-reference of items

Data Mining for Telecomm. Industry
(1)
 A rapidly expanding and highly competitive industry
and a great demand for data mining
 Understand the business involved
 Identify telecommunication patterns
 Catch fraudulent activities
 Make better use of resources
 Improve the quality of service
 Multidimensional analysis of telecommunication data
 Intrinsically multidimensional: calling-time, duration,
location of caller, location of callee, type of call, etc.

Data Mining for Telecomm. Industry
(2)
 Fraudulent pattern analysis and the identification of unusual
patterns

Identify potentially fraudulent users and their atypical usage
patterns
 Detect attempts to gain fraudulent entry to customer accounts

Discover unusual patterns which may need special attention
 Multidimensional association and sequential pattern analysis
 Find usage patterns for a set of communication services by
customer group, by month, etc.
 Promote the sales of specific services
 Improve the availability of particular services in a region
 Use of visualization tools in telecommunication data analysis

prototypes
 Summary

How to choose a data mining system?
 Commercial data mining systems have little in common

Different data mining functionality or methodology
 May even work with completely different kinds of
data sets
 Need multiple dimensional view in selection
 Data types: relational, transactional, text, time
sequence, spatial?
 System issues

running on only one or on several operating systems?
 a client/server architecture?

Provide Web-based interfaces and allow XML data as
input and/or output?

How to Choose a Data Mining System?
(2)
 Data sources
 ASCII text files, multiple relational data sources
 support ODBC connections (OLE DB, JDBC)?
 Data mining functions and methodologies
 One vs. multiple data mining functions
 One vs. variety of methods per function

More data mining functions and methods per function
provide the user with greater flexibility and analysis power
 Coupling with DB and/or data warehouse systems
 Four forms of coupling: no coupling, loose coupling,
semitight coupling, and tight coupling

Ideally, a data mining system should be tightly coupled with
a database system

How to Choose a Data Mining System?
(3)
 Scalability

Row (or database size) scalability

Column (or dimension) scalability

Curse of dimensionality: it is much more challenging
to make a system column scalable that row scalable
 Visualization tools

“A picture is worth a thousand words”

Visualization categories: data visualization, mining
result visualization, mining process visualization, and
visual data mining
 Data mining query language and graphical user interface

Easy-to-use and high-quality graphical user interface

Essential for user-guided, highly interactive data
mining

Examples of Data Mining Systems
(1)
 IBM Intelligent Miner

A wide range of data mining algorithms

Scalable mining algorithms

Toolkits: neural network algorithms, statistical
methods, data preparation, and data visualization tools

Tight integration with IBM's DB2 relational database
system
 SAS Enterprise Miner

A variety of statistical analysis tools

Data warehouse tools and multiple data mining
algorithms
 Mirosoft SQLServer 2000

Integrate DB and OLAP with mining

Support OLEDB for DM standard

Examples of Data Mining Systems
(2)
 SGI MineSet

Multiple data mining algorithms and advanced statistics

Advanced visualization tools
 Clementine (SPSS)
 An integrated data mining development environment for
end-users and developers
 Multiple data mining algorithms and visualization tools
 DBMiner (DBMiner Technology Inc.)
 Multiple data mining modules: discovery-driven OLAP
analysis, association, classification, and clustering
 Efficient, association and sequential-pattern mining
functions, and visual classification tool
 Mining both relational databases and data warehouses

prototypes
 Summary

Visual Data Mining
 Visualization: use of computer graphics to create visual
images which aid in the understanding of complex, often
massive representations of data
 Visual Data Mining: the process of discovering implicit but
useful knowledge from large data sets using visualization
techniques
 Purpose of Visualization
 Gain insight into an information space by mapping data onto
graphical primitives
 Provide qualitative overview of large data sets
 Search for patterns, trends, structure, irregularities, relationships
among data.
 Help find interesting regions and suitable parameters for further
quantitative analysis.
 Provide a visual proof of computer representations derived

Visual Data Mining & Data Visualization
 Integration of visualization and data mining
 data visualization
 data mining result visualization
 data mining process visualization
 interactive visual data mining
 Data visualization
 Data in a database or data warehouse can be
viewed

at different levels of granularity or abstraction

as different combinations of attributes or
dimensions
 Data can be presented in various visual forms

Boxplots from Statsoft: multiple
variable combinations

Data Mining Result Visualization
 Presentation of the results or knowledge obtained
from data mining in visual forms
 Examples
 Scatter plots and boxplots (obtained from
descriptive data mining)
 Decision trees
 Association rules
 Clusters
 Outliers
 Generalized rules

Visualization of data mining results in
SAS Enterprise Miner: scatter plots

Visualization of association rules
in MineSet 3.0

Visualization of a decision tree in
MineSet 3.0

Visualization of cluster groupings in
IBM Intelligent Miner

Data Mining Process Visualization
 Presentation of the various processes of data
mining in visual forms so that users can see
 How the data are extracted
 From which database or data warehouse they are
extracted
 How the selected data are cleaned, integrated,
preprocessed, and mined
 Which method is selected at data mining
 Where the results are stored
 How they may be viewed

Visualization of Data Mining
Processes by Clementine

Interactive Visual Data Mining
 Using visualization tools in the data mining process
to help users make smart data mining decisions
 Example
 Display the data distribution in a set of attributes
using colored sectors or columns (depending on
whether the whole space is represented by either
a circle or a set of columns)
 Use the display to which sector should first be
selected for classification and where a good split
point for this sector may be

Interactive Visual Mining by
Perception-Based Classification
(PBC)

Audio Data Mining
 Uses audio signals to indicate the patterns of data
or the features of data mining results
 An interesting alternative to visual mining
 An inverse task of mining audio (such as music)
databases which is to find patterns from audio data
 Visual data mining may disclose interesting
patterns using graphical displays, but requires
users to concentrate on watching patterns
 Instead, transform patterns into sound and music
and listen to pitches, rhythms, tune, and melody in
order to identify anything interesting or unusual

Scientific and Statistical Data Mining (1)
 There are many well-established statistical techniques for data
analysis, particularly for numeric data
 applied extensively to data from scientific experiments and data
from economics and the social sciences
 Regression
 predict the value of a response (dependent) variable from one or
more predictor (independent) variables where the variables are
numeric
 forms of regression: linear, multiple, weighted, polynomial,
nonparametric, and robust
 Generalized linear models
 allow a categorical response variable (or some transformation of
it) to be related to a set of predictor variables
 similar to the modeling of a numeric response variable using
linear regression
 include logistic regression and Poisson regression

 Regression trees

Binary trees used for classification and prediction

Similar to decision trees:Tests are performed at the internal nodes

Difference is at the leaf level

In a decision tree a majority voting is performed to assign a class label
to the leaf

In a regression tree the mean of the objective attribute is computed
and used as the predicted value
 Analysis of variance

Analyze experimental data for two or more populations described
by a numeric response variable and one or more categorical
variables (factors)
 Mixed-effect models

For analyzing grouped data, i.e. data that can be classified
according to one or more grouping variables

Typically describe relationships between a response variable and
some covariates in data grouped according to one or more factors

 Factor analysis

determine which vars are combined to generate a given factor

e.g., for many psychiatric data, one can indirectly measure other
quantities (such as test scores) that reflect the factor of interest
 Discriminant analysis

predict a categorical response variable, commonly used in social
science

Attempts to determine several discriminant functions (linear
combinations of the independent variables) that discriminate
among the groups defined by the response variable
 Time series: many methods such as autoregression, ARIMA
(Autoregressive integrated moving-average modeling), long
memory time-series modeling
 Survival analysis

predict the probability that a patient undergoing a medical
treatment would survive at least to time t (life span prediction)
 Quality control

display group summary charts

Theoretical Foundations of Data Mining
(1)
 Data reduction
 The basis of data mining is to reduce the data
representation
 Trades accuracy for speed in response
 Data compression
 The basis of data mining is to compress the given
data by encoding in terms of bits, association
rules, decision trees, clusters, etc.
 Pattern discovery
 The basis of data mining is to discover patterns
occurring in the database, such as associations,
classification models, sequential patterns, etc.

Theoretical Foundations of Data Mining
(2)
 Probability theory
 The basis of data mining is to discover joint probability
distributions of random variables
 Microeconomic view
 A view of utility: the task of data mining is finding
patterns that are interesting only to the extent in that
they can be used in the decision-making process of
some enterprise
 Inductive databases
 Data mining is the problem of performing inductive
logic on databases,
 The task is to query the data and the theory (i.e.,
patterns) of the database
 Popular among many researchers in database systems

Data Mining and Intelligent Query
Answering
 Query answering
 Direct query answering: returns exactly what is
being asked
 Intelligent (or cooperative) query answering:
analyzes the intent of the query and provides
generalized, neighborhood or associated
information relevant to the query
 Some users may not have a clear idea of exactly what
to mine or what is contained in the database
 Intelligent query answering analyzes the user's intent
and answers queries in an intelligent way

Data Mining and Intelligent Query
Answering (2)
 A general framework for the integration of data mining
and intelligent query answering

Data query: finds concrete data stored in a database

Knowledge query: finds rules, patterns, and other
kinds of knowledge in a database
 Ex. Three ways to improve on-line shopping service

Informative query answering by providing summary
information

Suggestion of additional items based on association
analysis

Product promotion by sequential pattern mining

prototypes
 Summary

Is Data Mining a Hype or
Will It Be Persistent?
 Data mining is a technology
 Technological life cycle
 Innovators
 Early adopters
 Chasm
 Early majority
 Late majority
 Laggards

Life Cycle of Technology
Adoption
 Data mining is at Chasm!?
 Existing data mining systems are too generic
 Need business-specific data mining solutions and
smooth integration of business logic with data
mining functions

Data Mining: Merely Managers'
Business or Everyone's?
 Data mining will surely be an important tool for
managers’ decision making
 Bill Gates: “Business @ the speed of thought”
 The amount of the available data is increasing, and data
mining systems will be more affordable
 Multiple personal uses
 Mine your family's medical history to identify
genetically-related medical conditions
 Mine the records of the companies you deal with
 Mine data on stocks and company performance, etc.
 Invisible data mining
 Build data mining functions into many intelligent tools

Social Impacts: Threat to Privacy
and Data Security?
 Is data mining a threat to privacy and data security?
 “Big Brother”, “Big Banker”, and “Big Business” are
carefully watching you
 Profiling information is collected every time

You use your credit card, debit card, supermarket loyalty card, or
frequent flyer card, or apply for any of the above

You surf the Web, reply to an Internet newsgroup, subscribe to a
magazine, rent a video, join a club, fill out a contest entry form,

You pay for prescription drugs, or present you medical care
number when visiting the doctor
 Collection of personal data may be beneficial for
companies and consumers, there is also potential for
misuse

Protect Privacy and Data
Security
 Fair information practices
 International guidelines for data privacy protection
 Cover aspects relating to data collection, purpose,
use, quality, openness, individual participation, and
accountability
 Purpose specification and use limitation
 Openness: Individuals have the right to know what
information is collected about them, who has
access to the data, and how the data are being used
 Develop and use data security-enhancing techniques
 Blind signatures
 Biometric encryption
 Anonymous databases

prototypes
 Summary

(1)
 Application exploration
 development of application-specific data mining
system
 Invisible data mining (mining as built-in function)
 Scalable data mining methods
 Constraint-based mining: use of constraints to
guide data mining systems in their search for
interesting patterns
 Integration of data mining with database systems,
data warehouse systems, and Web database
systems

Trends in Data Mining (2)
 Standardization of data mining language

A standard will facilitate systematic development,
improve interoperability, and promote the education
and use of data mining systems in industry and
society
 Visual data mining
 New methods for mining complex types of data

More research is required towards the integration of
data mining methods with existing data analysis
techniques for the complex types of data
 Web mining
 Privacy protection and information security in data
mining

prototypes
 Summary

Summary
 Domain-specific applications include biomedicine (DNA),
finance, retail and telecommunication data mining
 There exist some data mining systems and it is important
to know their power and limitations
 Visual data mining include data visualization, mining
result visualization, mining process visualization and
interactive visual mining
 There are many other scientific and statistical data mining
methods developed but not covered in this book
 Also, it is important to study theoretical foundations of
data mining
 Intelligent query answering can be integrated with mining
 It is important to watch privacy and security issues in data
mining

References (1)
 M. Ankerst, C. Elsen, M. Ester, and H.-P. Kriegel. Visual classification: An interactive
approach to decision tree construction. KDD'99, San Diego, CA, Aug. 1999.
 P. Baldi and S. Brunak. Bioinformatics: The Machine Learning Approach. MIT Press,
1998.
 S. Benninga and B. Czaczkes. Financial Modeling. MIT Press, 1997.
 L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression
Trees. Wadsworth International Group, 1984.
 M. Berthold and D. J. Hand. Intelligent Data Analysis: An Introduction. Springer-
Verlag, 1999.
 M. J. A. Berry and G. Linoff. Mastering Data Mining: The Art and Science of Customer
Relationship Management. John Wiley & Sons, 1999.
 A. Baxevanis and B. F. F. Ouellette. Bioinformatics: A Practical Guide to the Analysis
of Genes and Proteins. John Wiley & Sons, 1998.
 Q. Chen, M. Hsu, and U. Dayal. A data-warehouse/OLAP framework for scalable
telecommunication tandem traffic analysis. ICDE'00, San Diego, CA, Feb. 2000.
 W. Cleveland. Visualizing Data. Hobart Press, Summit NJ, 1993.
 S. Chakrabarti, S. Sarawagi, and B. Dom. Mining surprising patterns using temporal
description length. VLDB'98, New York, NY, Aug. 1998.

References (2)
 J. L. Devore. Probability and Statistics for Engineering and the Science, 4th ed. Duxbury
Press, 1995.
 A. J. Dobson. An Introduction to Generalized Linear Models. Chapman and Hall, 1990.
 B. Gates. Business @ the Speed of Thought. New York: Warner Books, 1999.
 M. Goebel and L. Gruenwald. A survey of data mining and knowledge discovery
software tools. SIGKDD Explorations, 1:20-33, 1999.
 D. Gusfield. Algorithms on Strings, Trees and Sequences, Computer Science and
Computation Biology. Cambridge University Press, New York, 1997.
 J. Han, Y. Huang, N. Cercone, and Y. Fu. Intelligent query answering by knowledge
discovery techniques. IEEE Trans. Knowledge and Data Engineering, 8:373-390, 1996.
 R. C. Higgins. Analysis for Financial Management. Irwin/McGraw-Hill, 1997.
 C. H. Huberty. Applied Discriminant Analysis. New York: John Wiley & Sons, 1994.
 T. Imielinski and H. Mannila. A database perspective on knowledge discovery.
Communications of ACM, 39:58-64, 1996.
 D. A. Keim and H.-P. Kriegel. VisDB: Database exploration using multidimensional
visualization. Computer Graphics and Applications, pages 40-49, Sept. 94.

References (3)
 J. M. Kleinberg, C. Papadimitriou, and P. Raghavan. A microeconomic view of data
mining. Data Mining and Knowledge Discovery, 2:311-324, 1998.
 H. Mannila. Methods and problems in data mining. ICDT'99 Delphi, Greece, Jan. 1997.
 R. Mattison. Data Warehousing and Data Mining for Telecommunications. Artech
House, 1997.
 R. G. Miller. Survival Analysis. New York: Wiley, 1981.
 G. A. Moore. Crossing the Chasm: Marketing and Selling High-Tech Products to
Mainstream Customers. Harperbusiness, 1999.
 R. H. Shumway. Applied Statistical Time Series Analysis. Prentice Hall, 1988.
 E. R. Tufte. The Visual Display of Quantitative Information. Graphics Press, Cheshire,
CT, 1983.
 E. R. Tufte. Envisioning Information. Graphics Press, Cheshire, CT, 1990.
 E. R. Tufte. Visual Explanations : Images and Quantities, Evidence and Narrative.
Graphics Press, Cheshire, CT, 1997.
 M. S. Waterman. Introduction to Computational Biology: Maps, Sequences, and
Genomes (Interdisciplinary Statistics). CRC Press, 1995.

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data mining and the purpose of using mining

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