Exploring overlapping clusters using dynamic re-scaling and sampling

作者:Mei Kobayashi, Masaki Aono

摘要

Until recently, the aim of most text-mining work has been to understand major topics and clusters. Minor topics and clusters have been relatively neglected even though they may represent important information on rare events. We present a novel method for exploring overlapping clusters of heterogeneous sizes, which is based on vector space modeling, covariance matrix analysis, random sampling, and dynamic re-weighting of document vectors in massive databases. Our system addresses a combination of difficult issues in database analysis, such as synonymy and polysemy, identification of minor clusters, accommodation of cluster overlap, automatic labeling of clusters based on their document contents, and the user-controlled trade-off between speed of computation and quality of results. We conducted implementation studies with new articles from the Reuters and LA Times TREC data sets and artificially generated data with a known cluster structure to demonstrate the effectiveness of our system.

论文关键词:Clustering, Covariance matrix analysis, Text mining, Dimensional reduction, Latent semantic indexing (LSI), Principal component analysis (PCA), Random sampling, Vector space modeling

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论文官网地址:https://doi.org/10.1007/s10115-006-0005-y