Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 4 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 6 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 7