1. Data Mining: Algorithms and Principles CS512 Midterm Coverage and Review Outlines
Jiawei Han
Department of Computer Science
University of Illinois at Urbana-Champaign
September 22, 2018
Data Mining: Pirnciples, Algorithms and Applications

2. Data Mining: Pirnciples, Algorithms and Applications
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
September 22, 2018
Data Mining: Pirnciples, Algorithms and Applications

3. Data Mining: Pirnciples, Algorithms and Applications
Mining Data Streams
What is stream data? Why stream data mining?
Stream data management systems: Issues and solutions
Methods for approximate query answering
Stream data cube and multidimensional OLAP analysis
A stream cube architecture and implementation methods
Stream frequent pattern analysis
Lossy counting method for mining frequent itemsets
Stream classification
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
September 22, 2018
Data Mining: Pirnciples, Algorithms and Applications

4. 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
September 22, 2018
Data Mining: Pirnciples, Algorithms and Applications

5. 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
The gIndex algorithm
September 22, 2018
Data Mining: Pirnciples, Algorithms and Applications

6. Mining Spatial and Multimedia data
Spatial Database Systems (SDBMS)
spatial data types, queries and query processing
Spatial Data Warehousing
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
September 22, 2018
Data Mining: Pirnciples, Algorithms and Applications

7. 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, look-one-ahead, negative tuple sampling
Performance: reasoning on efficiency and accuracy
September 22, 2018
Data Mining: Pirnciples, Algorithms and Applications