01Intro数据挖掘第三版引论.ppt

* * * * * * * * * * * * * * * Add a definition/description of “traditional data analysis”. * * * * * * * * * * * Data Mining Function: (4) Cluster Analysis Unsupervised learning (i.e., Class label is unknown) Group data to form new categories (i.e., clusters), e.g., cluster houses to find distribution patterns Principle: Maximizing intra-class similarity minimizing interclass similarity Many methods and applications * Data Mining Function: (5) Outlier Analysis Outlier analysis Outlier: A data object that does not comply with the general behavior of the data Noise or exception? ― One person’s garbage could be another person’s treasure Methods: by product of clustering or regression analysis, … Useful in fraud detection, rare events analysis * Time and Ordering: Sequential Pattern, Trend and Evolution Analysis Sequence, trend and evolution analysis Trend, time-series, and deviation analysis: e.g., regression and value prediction Sequential pattern mining e.g., first buy digital camera, then buy large SD memory cards Periodicity analysis Motifs and biological sequence analysis Approximate and consecutive motifs Similarity-based analysis Mining data streams Ordered, time-varying, potentially infinite, data streams * Structure and Network Analysis Graph mining Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments) Information network analysis Social networks: actors (objects, nodes) and relationships (edges) e.g., author networks in CS, terrorist networks Multiple heterogeneous networks A person could be multiple information networks: friends, family, classmates, … Links carry a lot of semantic information: Link mining Web mining Web is a big information network: from PageRank to Google Analysis of Web information networks Web community discovery, opinion mining, usage mining, … * Evaluation of Knowledge Are all mined knowledge interesting? One can mine tremendous amount of “patterns” Some may fit only certain dimension spa