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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
1
Outline

Stream Data Mining

Mining time series and sequence data

Graph and structured pattern mining

Mining spatial, spatiotemporal and multimedia
data

Multi-relational and cross-database data mining
May 22, 2017
Data Mining: Pirnciples, Algorithms and Applications
2
Mining Data Streams

What is stream data? Why stream data mining?

Stream data management systems: Issues and solutions


Stream data cube and multidimensional OLAP analysis


Lossy counting method for mining frequent itemsets
Stream classification


A stream cube architecture and implementation methods
Stream frequent pattern analysis


Methods for approximate query answering
Decision tree induction method for dynamic data streams
Stream cluster analysis

K-median based method for clustering data streams

CluStream method for clustering evolving data streams
May 22, 2017
Data Mining: Pirnciples, Algorithms and Applications
3
Time-Series and Sequential Pattern
Mining




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

Graph pattern mining and its applications

Frequent subgraph mining and closed graph pattern
mining


The gSpan algorithm

The CloseGraph algorithm
Graph indexing techniques

Indexing by discriminative and frequent pattern
analysis

May 22, 2017
The gIndex algorithm
Data Mining: Pirnciples, Algorithms and Applications
5
Mining Spatial and Multimedia data

Spatial Database Systems (SDBMS)


Spatial Data Warehousing



spatial data types, queries and query processing
Spatial OLAP (models and implementations)
Spatial Data Mining

Spatial association and co-location rule mining

Spatial classification and clustering

Spatial outlier detection
Mining multimedia databases

Content-based retrieval and similarity search

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

Classification over multiple-relations in databases


Motivation and major challenges
The CrossMine algorithm

Major ideas: TID propagation, rule generation, lookone-ahead, negative tuple sampling

May 22, 2017
Performance: reasoning on efficiency and accuracy
Data Mining: Pirnciples, Algorithms and Applications
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