Dr.

Rajarshi Roy Chowdhury

Assistant Professor
Department of Computer Science
rajarshi@aiub.edu

My research areas:
• Internet of Things
• Machine Learning
• Networking
• Data Mining

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Data Warehousing and
Data Mining

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 Data Mining:
Concepts and Techniques
(3rd ed.)

— Chapter 1 —

© Jiawei Han, Micheline Kamber, and Jian Pei

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 Chapter 1. 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

◼ A Brief History of Data Mining and Data Mining Society

◼ Summary
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 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, Facebook and
other social media
◼ We are drowning in data but starving for knowledge!
◼ “Necessity is the mother of invention”—Data mining—Automated analysis of
massive data sets
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Evolution of Database Technology

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 Evolution of Database Technology
◼ 1960s:
◼ Data collection, database creation, IMS and network DBMS
◼ 1970s:
◼ Relational data model, relational DBMS implementation
◼ 1980s:
◼ RDBMS, advanced data models (extended-relational, OO, 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
◼ Big Data
◼ 2010s - Present:
◼ Distributed and Decentralized Dabatases
◼ Al and ML Integration
◼ Graph Databases
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Evolution of Machine Learning

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 Chapter 1. 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

◼ A Brief History of Data Mining and Data Mining Society

◼ Summary
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 What Is Data Mining?

◼ Data mining (knowledge discovery from data)

◼ Extraction of interesting (non-trivial, implicit, previously unknown
and potentially useful) patterns or knowledge from huge amount of
data
◼ Alternative names
◼ Knowledge discovery in databases (KDD), knowledge extraction,
data/pattern analysis, information harvesting, business intelligence,
etc.

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 Knowledge Discovery (KDD) Process
◼ This is a view from typical database
systems and data warehousing
Pattern Evaluation
communities
◼ Data mining plays an essential role in
the knowledge discovery process
Data Mining

Task-relevant Data

Data Warehouse Selection

Data Cleaning

Data Integration

Databases
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 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

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 Data Mining in Business Intelligence

Increasing potential
to support
business decisions End User
Decision
Making

Data Presentation Business

Analyst
Visualization Techniques
Data Mining Data
Information Discovery Analyst

Data Exploration
Statistical Summary, Querying, and Reporting

Data Preprocessing/Integration, Data Warehouses

DBA
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems
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 KDD Process: A Typical View from ML and
Statistics

Input Data Data Pre- Data Post-

Processing Mining Processing

Data integration Pattern discovery- Pattern evaluation

Normalization Association & correlation Pattern selection
Feature selection Classification Pattern interpretation
Clustering
Dimension reduction Pattern visualization
Outlier analysis
…………

◼ This is a view from typical machine learning and statistics communities

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 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

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 Chapter 1. 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

◼ A Brief History of Data Mining and Data Mining Society

◼ Summary
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 Multi-Dimensional View of Data Mining
◼ Data to be mined
◼ Database data (extended-relational, object-oriented, heterogeneous), 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 etc.

◼ Applications adapted
◼ Retail, telecommunication, banking, fraud analysis, bio-data mining, stock

market analysis, text mining, Web mining, etc.

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 Chapter 1. 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

◼ A Brief History of Data Mining and Data Mining Society

◼ Summary
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 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
◼ Spatial data and spatiotemporal data
◼ Multimedia database
◼ Text databases
◼ The World-Wide Web

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 Chapter 1. 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

◼ A Brief History of Data Mining and Data Mining Society

◼ Summary
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 Data Mining Function: 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

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 Data Mining Function: Association and
Correlation Analysis
◼ Frequent patterns (or frequent item-sets)
◼ 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?
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 Data Mining Function: 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, logistic regression, …
◼ Typical applications:
◼ Credit card fraud detection, direct marketing, classifying stars, diseases,
web-pages, …

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 Data Mining Function: 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
Methods- K-Means, DBSCAN..etc.
Applications: Market segmentation, Social network analysis,
Document clustering, anomaly detection…etc.

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 Data Mining Function: 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

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Time and Ordering: Sequential Pattern,
Trend and Evolution Analysis
◼ Sequence, trend and evolution analysis
◼ Trend, time-series, and deviation analysis: e.g., regression and

value prediction
◼ Sequential pattern mining

◼ e.g., first buy digital camera, then buy large SD memory

cards
◼ Periodicity analysis, e.g to determine peak visitor times during

the week
◼ Biological sequence analysis, e.g examining biological
sequences
◼ Similarity-based analysis, e.g document clustering

◼ Mining data streams

◼ Ordered, time-varying, potentially infinite, data streams
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 Structure and Network Analysis

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

substructures (web fragments)

◼ Information network analysis
◼ Social networks: actors (objects, nodes) and relationships (edges)

◼ e.g., author networks in CS, terrorist networks

◼ Multiple heterogeneous networks

◼ A person could be multiple information networks: friends, family,

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

◼ Web mining
◼ Web is a big information network: from PageRank to Google

◼ Analysis of Web information networks

◼ Web community discovery, opinion mining, usage mining, …

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 Chapter 1. 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

◼ A Brief History of Data Mining and Data Mining Society

◼ Summary
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Data Mining: Confluence of Multiple Disciplines

Machine Pattern Statistics

Learning Recognition

Applications Data Mining Visualization

Algorithm Database High-Performance

Technology Computing

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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

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 Chapter 1. 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

◼ A Brief History of Data Mining and Data Mining Society

◼ Summary
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 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
◼ Software engineering

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 Chapter 1. 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

◼ A Brief History of Data Mining and Data Mining Society

◼ Summary
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 Major Issues in Data Mining (1)

◼ Mining Methodology
◼ Mining various and new kinds of knowledge
◼ Mining knowledge in multi-dimensional space
◼ 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

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 Major Issues in Data Mining (2)

◼ Efficiency and Scalability

◼ Efficiency and scalability of data mining algorithms
◼ Parallel, distributed, stream, and incremental mining methods
◼ Diversity of data types
◼ Handling complex types of data
◼ Mining dynamic, networked, and global data repositories
◼ Data mining and society
◼ Social impacts of data mining
◼ Privacy-preserving data mining

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 Chapter 1. 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

◼ A Brief History of Data Mining and Data Mining Society

◼ Summary
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 A Brief History of Data Mining Society

◼ 1989 IJCAI Workshop on Knowledge Discovery in Databases

◼ Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)
◼ 1991-1994 Workshops on Knowledge Discovery in Databases
◼ Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-
Shapiro, P. Smyth, and R. Uthurusamy, 1996)
◼ 1995-1998 International Conferences on Knowledge Discovery in Databases and Data
Mining (KDD’95-98)
◼ Journal of Data Mining and Knowledge Discovery (1997)
◼ ACM SIGKDD conferences since 1998 and SIGKDD Explorations
◼ More conferences on data mining
◼ PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM
(2001), etc.
◼ ACM Transactions on KDD starting in 2007

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 Conferences and Journals on Data Mining

◼ KDD Conferences ◼ Other related conferences

◼ ACM SIGKDD Int. Conf. on ◼ DB conferences: ACM SIGMOD,
Knowledge Discovery in Databases
VLDB, ICDE, EDBT, ICDT, …
and Data Mining (KDD)
◼ SIAM Data Mining Conf. (SDM) ◼ Web and IR conferences: WWW,
SIGIR, WSDM
◼ (IEEE) Int. Conf. on Data Mining
(ICDM) ◼ ML conferences: ICML, NIPS
◼ European Conf. on Machine Learning ◼ PR conferences: CVPR,
and Principles and practices of ◼ Journals
Knowledge Discovery and Data
◼ Data Mining and Knowledge
Mining (ECML-PKDD)
Discovery (DAMI or DMKD)
◼ Pacific-Asia Conf. on Knowledge
Discovery and Data Mining ◼ IEEE Trans. On Knowledge and
(PAKDD) Data Eng. (TKDE)
◼ Int. Conf. on Web Search and Data ◼ KDD Explorations
Mining (WSDM) ◼ ACM Trans. on KDD

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Where to Find References? DBLP, CiteSeer, Google

◼ Data mining and KDD (SIGKDD: CDROM)

◼ Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.
◼ Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD
◼ Database systems (SIGMOD: ACM SIGMOD Anthology—CD ROM)
◼ Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA
◼ Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc.
◼ AI & Machine Learning
◼ Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc.
◼ Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-
PAMI, etc.
◼ Web and IR
◼ Conferences: SIGIR, WWW, CIKM, etc.
◼ Journals: WWW: Internet and Web Information Systems,
◼ Statistics
◼ Conferences: Joint Stat. Meeting, etc.
◼ Journals: Annals of statistics, etc.
◼ Visualization
◼ Conference proceedings: CHI, ACM-SIGGraph, etc.
◼ Journals: IEEE Trans. visualization and computer graphics, etc.
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 Chapter 1. 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

◼ A Brief History of Data Mining and Data Mining Society

◼ Summary
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 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

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 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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