UNIT2-Data Mining.pdf

2/11/2023 6:11:44 PM Dr. Nancy Kumari 1
Introduction to Data Mining:
Concepts and Techniques
— Unit 2—
Dr. Nancy Kumari
(School of Computer Science and Engineering)
1

Unit 2. Introduction
 Why Data Mining?
 What Is Data Mining?
 A Multi-Dimensional View of Data Mining
 What Kind of Data Can Be Mined?
 What Kinds of Patterns Can Be Mined?
 What Technology Are Used?
 What Kind of Applications Are Targeted?
 Major Issues in Data Mining
 Integration of a data mining system with a database or data warehouse system.
 Summary

Why Data Mining?
 The Explosive Growth of Data: from terabytes to petabytes
 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 analysis of
massive data sets

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!

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

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?
 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

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

Example: A Web Mining Framework
 Web mining usually involves
 Data cleaning
 Data integration from multiple sources
 Warehousing the data
 Data cube construction
 Data selection for data mining
 Data mining
 Presentation of the mining results
 Patterns and knowledge to be used or stored into knowledge-
base

Data Mining in Business Intelligence
Increasing potential
to support
business decisions End User
Business
Analyst
Data
Analyst
DBA
Decision
Making
Data Presentation
Visualization Techniques
Data Mining
Information Discovery
Data Exploration
Statistical Summary, Querying, and Reporting
Data Preprocessing/Integration, Data Warehouses
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems

Example: Mining vs. Data Exploration
 Business intelligence view
 Warehouse, data cube, reporting but not
much mining
 Business objects vs. data mining tools
 Supply chain example: tools
 Data presentation
 Exploration

KDD Process: A Typical View from ML and
Statistics
Input Data Data
Mining
Data Pre-
Processing
Post-
Processing
Data integration
Normalization
Feature selection
Dimension reduction
Pattern discovery
Association & correlation
Classification
Clustering
Outlier analysis
… … … …
Pattern evaluation
Pattern selection
Pattern interpretation
Pattern visualization

Example: Medical Data Mining
 Health care & medical data mining – often adopted
such a view in statistics and machine learning
 Preprocessing of the data (including feature extraction
and dimension reduction)
 Classification or/and clustering processes
 Post-processing for presentation

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
 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.

Data Mining: On What Kinds of Data?
 Database-oriented data sets and applications
 Relational database, data warehouse, transactional database
 Advanced data sets and advanced applications
 Data streams and sensor data
 Time-series data, temporal data, sequence data (incl. bio-sequences)
 Structure data, graphs, social networks and multi-linked data
 Object-relational databases
 Heterogeneous databases and legacy databases
 Spatial data and spatiotemporal data
 Multimedia database
 Text databases
 The World-Wide Web

Data Mining Function: (1) Generalization
 Information integration and data warehouse construction
 Data cleaning, transformation, integration, and
multidimensional data model
 Data cube technology
 Scalable methods for computing (i.e., materializing)
multidimensional aggregates
 OLAP (online analytical processing)
 Multidimensional concept description: Characterization and
discrimination
 Generalize, summarize, and contrast data characteristics,
e.g., dry vs. wet region

Data Mining Function: (2) Association and
Correlation Analysis
 Frequent patterns (or frequent itemsets)
 What items are frequently purchased together in your
Walmart?
 Association, correlation vs. causality
 A typical association rule
 Diaper  Beer [0.5%, 75%] (support, confidence)
 Are strongly associated items also strongly correlated?
 How to mine such patterns and rules efficiently in large
datasets?
 How to use such patterns for classification, clustering, and
other applications?

Data Mining Function: (3) Classification
 Classification and label prediction
 Construct models (functions) based on some training examples
 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, …

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.
 Many methods and applications.

Data Mining Function: (5) Outlier Analysis
 Outlier analysis
 Outlier: A data object that does not comply
with the general behavior of the data.
 Noise or exception? ― One person’s garbage
could be another person’s treasure.
 Methods: by product of clustering or
regression analysis, …
 Useful in fraud detection, rare events analysis.

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
 Motifs and biological sequence analysis
 Approximate and consecutive motifs
 Similarity-based analysis
 Mining data streams
 Ordered, time-varying, potentially infinite, data streams

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, …

Evaluation of Knowledge
 Are all mined knowledge interesting?
 One can mine tremendous amount of “patterns” and knowledge
 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
 …

Data Mining: What Technology Are Used?
Data Mining
Machine
Learning
Statistics
Applications
Algorithm
Pattern
Recognition
High-Performance
Computing
Visualization
Database
Technology

Why Confluence of Multiple Disciplines?
 Tremendous amount of data
 Algorithms must be highly scalable to handle such as tera-bytes of 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 networks and multi-linked data
 Heterogeneous databases and legacy databases
 Spatial, spatiotemporal, multimedia, text and Web data
 Software programs, scientific simulations
 New and sophisticated applications

Applications of Data Mining
 Web page analysis: from web page classification, clustering to
PageRank & HITS algorithms
 Collaborative analysis & recommender systems
 Basket data analysis to targeted marketing
 Biological and medical data analysis: classification, cluster analysis
(microarray data analysis), biological sequence analysis, biological
network analysis
 Data mining and software engineering (e.g., IEEE Computer, Aug.
2009 issue)
 From major dedicated data mining systems/tools (e.g., SAS, MS SQL-
Server Analysis Manager, Oracle Data Mining Tools) to invisible data
mining

Major Issues in Data Mining (1)
 Mining Methodology
 Mining various and new kinds of knowledge
 Mining knowledge in multi-dimensional space
 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
 Incorporation of background knowledge
 Presentation and visualization of data mining results

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

Integration of a data mining system with a
database or data warehouse system.
 The data mining system is integrated with a database or data
warehouse system so that it can do its tasks in an effective presence.
A data mining system operates in an environment that needed it to
communicate with other data systems like a database system. There
are the possible integration schemes that can integrate these systems
which are as follows −
 No coupling − No coupling defines that a data mining system
will not use any function of a database or data warehouse system.
It can retrieve data from a specific source (including a file
system), process data using some data mining algorithms, and
therefore save the mining results in a different file.

database or data warehouse system(cont.)
• Such a system, though simple, deteriorates from various
limitations. First, a Database system offers a big deal of
flexibility and adaptability at storing, organizing, accessing, and
processing data. Without using a Database/Data warehouse
system, a Data mining system can allocate a large amount of time
finding, collecting, cleaning, and changing data.
 Loose Coupling − In this data mining system uses some services
of a database or data warehouse system. The data is fetched from
a data repository handled by these systems. It is better than no
coupling as it can fetch some area of data stored in databases by
using query processing or various system facilities. Parallel,
distributed, stream, and incremental mining methods

database or data warehouse system(cont.)
 Semitight Coupling − In this adequate execution of a few essential
data mining primitives can be supported in the database/data ware
house system. These primitives can contain sorting, indexing,
aggregation, histogram analysis, multi-way join, and pre-
computation of some important statistical measures, including sum,
count, max, min, standard deviation, etc.
 Tight coupling − Tight coupling defines that a data mining system
is smoothly integrated into the database/data warehouse system. The
data mining subsystem is considered as one functional element of an
information system.

Summary
 Data mining: Discovering interesting patterns and knowledge from
massive amount of data
 A natural evolution of database 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, outlier and trend analysis, etc.
 Data mining technologies and applications
 Major issues in data mining

Recommended Reference Books
 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 and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd ed., 2011
 D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001
 T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd
ed., Springer-Verlag, 2009
 B. Liu, Web Data Mining, Springer 2006.
 T. M. Mitchell, Machine Learning, McGraw Hill, 1997
 G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991
 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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