Topic 1: Principles of parallel computing The motivation of parallel and distributed computing, parallel computing enviorement, principles of parallel computing, parallel programming
Topic 2: Distributed computing Distributed computing architecture, characteristics, the difference with parallel computing
Topic 3: High performance data mining techniques Pros of parallel data mining, benifits, SPRINT, SMP-based desicion tree, parallel K-means, parallel associaition rules mining (CCPD, CountDist), Parallel sequence mining(GSP), etc.
Topic 4: Stream data mining Definition of stream, model, characteristics, the main factors in stream mining, K-means based stream clustering, Decision tree based stream classification, frequent mining on multiple time granularities£¬change detection in streams, etc.
Topic 5: Text mining Text mining vs. information retrieval, data structure, measures for text retrieval, keywords associations rules mining, text classification, text clustering
Topic 6: Web mining Characteristics of Web mining, Web document classification, Web usage mining
Topic 7: Aapplications Credit scoring, business intelligence, scientific simulation
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