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Hierarchical Document Clustering using Frequent Itemsets 分层文件使用频繁项集聚类
Hierarchical Document Clusteringusing Frequent Itemsets Benjamin C. M. Fung, Ke Wang, Martin EsterSDM 2003 Presentation Serhiy Polyakov DSCI 5240 Fall 2005 Introduction Application of document clustering: web mining search engines information retrieval topological analysis Special requirements for document clustering: high dimensionality high volume of data ease for browsing meaningful cluster labels Problem statement Problems with some standard clustering techniques: number of clusters is unknown size of the clusters varies greatly Suggested approach - Frequent Itemset-based Hierarchical Clustering (FIHC): Reduced dimensionality High clustering accuracy Number of clusters as an optional input parameter Easy to browse with meaningful cluster description Algorithm FIHC preprocessing steps: stop words removal stemming on the document set each document is represented by a vector of frequencies of remaining items within the document FIHC two main steps: Constructing Initial Clusters (construct an initial cluster to contain all the documents that contain each global frequent itemset) Making Clusters Disjoint (after this step, each document belongs to exactly one cluster) Example of Disjoined clusters Building the Cluster Tree The set of clusters produced by the previous stage can be viewed as a set of topics and subtopics in the document set. A cluster (topic) tree is constructed based on the similarity among clusters Tree Structure vs Browsing Deep hierarchy tree produced by other methods may not be suitable for browsing A flat hierarchy reduces the number of navigation steps which in turn decreases the chance for a user to make mistakes If a hierarchy is too flat, a parent topic may contain too many subtopics and it would increase the time and difficulty for the user to locate her target A balance between depth and width of the tree is essential for browsing Evaluation Evaluation has been performed in terms of F-measure The following parameters have be
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