Topic(1)-Intro data mining master ALEX.pptx

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Business Intelligence and Data
Mining
— Topic 1 —

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Topic 1. Introduction
• Why Data Mining?
• What Is Data Mining?
• A Multi-Dimensional View of Data Mining
• What Kinds of Data Can Be Mined?
• What Kinds of Patterns Can Be Mined?
• What Kinds of Technologies Are Used?
• What Kinds 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
• 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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• 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
– 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 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

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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
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

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KDD Process: A Typical View from ML and Statistics
Input Data Data
Mining
Data Pre-
Processing
Post-
Processing
• This is a view from typical machine learning and statistics communities
Data integration
Normalization
Feature selection
Dimension reduction
Pattern discovery
Association & correlation
Classification
Clustering
Outlier analysis
… … … …
Pattern evaluation
Pattern selection
Pattern interpretation
Pattern visualization

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Which View Do You Prefer?
• Which view do you prefer?
– KDD vs. ML/Stat. vs. Business Intelligence
– Depending on the data, applications, and your focus
• Data Mining vs. Data Exploration
– Business intelligence view
• Warehouse, data cube, reporting but not much mining
– Business objects vs. data mining tools
– Supply chain example: mining vs. OLAP vs. presentation tools
– Data presentation vs. data exploration

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• Summary

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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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• Summary

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Data Mining: On What Kinds of Data?
• Database-oriented data sets and applications
– Relational database, data warehouse, transactional database
– Object-relational databases, Heterogeneous databases and legacy databases
• 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 information networks
– Spatial data and spatiotemporal data
– Multimedia database
– Text databases
– The World-Wide Web

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• Summary

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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, and
other applications?

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

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• Summary

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Data Mining: Confluence of Multiple Disciplines
Data Mining
Machine
Learning
Statistics
Applications
Algorithm
Pattern
Recognition
High-Performance
Computing
Visualization
Database
Technology

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Why Confluence of Multiple Disciplines?
• Tremendous amount of data
– Algorithms must be scalable to handle big 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 and information networks
– Spatial, spatiotemporal, multimedia, text and Web data
– Software programs, scientific simulations
• New and sophisticated applications

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• 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
• Data mining and software engineering
• 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 science and information 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, trend and outlier analysis, etc.
• Data mining technologies and applications
• Major issues in data mining

Topic(1)-Intro data mining master ALEX.pptx

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