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Data Mining:
Algorithms and Principles
CS512 Midterm Coverage and Review
Outlines
Jiawei Han
Department of Computer Science
University of Illinois at Urbana-Champaign
www.cs.uiuc.edu/~hanj
May 22, 2017
Data Mining: Pirnciples, Algorithms and Applications
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Outline
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Stream Data Mining
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Mining time series and sequence data
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Graph and structured pattern mining
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Mining spatial, spatiotemporal and multimedia
data
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Multi-relational and cross-database data mining
May 22, 2017
Data Mining: Pirnciples, Algorithms and Applications
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Mining Data Streams
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What is stream data? Why stream data mining?
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Stream data management systems: Issues and solutions
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Stream data cube and multidimensional OLAP analysis
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Lossy counting method for mining frequent itemsets
Stream classification
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A stream cube architecture and implementation methods
Stream frequent pattern analysis
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Methods for approximate query answering
Decision tree induction method for dynamic data streams
Stream cluster analysis
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K-median based method for clustering data streams
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CluStream method for clustering evolving data streams
May 22, 2017
Data Mining: Pirnciples, Algorithms and Applications
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Time-Series and Sequential Pattern
Mining
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Regression and trend analysis
 Trend discovery in time-series
Similarity search in time-series analysis
 Similarity search and subsequence matching
Sequential pattern mining algorithms
 Sequential pattern vs. closed sequential pattern
 Efficient mining of sequential patterns: CloSpan vs.
PrefixSpan vs. Spade vs. GSP
Markov chain and hidden Markov model
 Markov chain models, first-order vs. higher order, and
their applications
 Learning and prediction using HMM
May 22, 2017
Data Mining: Pirnciples, Algorithms and Applications
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Graph and Structured Pattern Mining
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Graph pattern mining and its applications
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Frequent subgraph mining and closed graph pattern
mining
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The gSpan algorithm
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The CloseGraph algorithm
Graph indexing techniques
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Indexing by discriminative and frequent pattern
analysis
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May 22, 2017
The gIndex algorithm
Data Mining: Pirnciples, Algorithms and Applications
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Mining Spatial and Multimedia data
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Spatial Database Systems (SDBMS)
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Spatial Data Warehousing
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spatial data types, queries and query processing
Spatial OLAP (models and implementations)
Spatial Data Mining
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Spatial association and co-location rule mining
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Spatial classification and clustering
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Spatial outlier detection
Mining multimedia databases
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Content-based retrieval and similarity search
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Progressive deepening at mining multimedia databases
May 22, 2017
Data Mining: Pirnciples, Algorithms and Applications
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Multi-Relational and Multi-DB Mining
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Classification over multiple-relations in databases
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Motivation and major challenges
The CrossMine algorithm
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Major ideas: TID propagation, rule generation, lookone-ahead, negative tuple sampling
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May 22, 2017
Performance: reasoning on efficiency and accuracy
Data Mining: Pirnciples, Algorithms and Applications
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