INFS 323 – Data Warehousing & Data Mining
TOPICS CHAPTER
Introduction to Data Mining 1
Data Preprocessing 2
Data Warehouse and OLAP Technology 3
Mining Frequent Patterns & Associations 4
Classification & Prediction 5
Cluster Analysis 6
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Data Mining:
Concepts and Techniques
(3rd ed.)
— Chapter 1 —
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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
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
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
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
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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
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
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Knowledge Discovery (KDD) Process
This is a view from typical
database systems and data
Pattern Evaluation
warehousing 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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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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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.
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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 and legacy databases
Spatial data and spatiotemporal data
Multimedia database
Text databases
The World-Wide Web
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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
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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,
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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, …
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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
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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
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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
Motifs and biological sequence analysis
Approximate and consecutive motifs
Similarity-based analysis
Mining data streams
Ordered, time-varying, potentially infinite, data streams
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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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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
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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
Invisible data mining
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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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