A quality driven Hierarchical Data Divisive Soft Clustering for information retrieval

作者：Bordogna Gloria; Pasi Gabriella^*

来源：Knowledge-Based Systems, 2012, 26: 9-19.

DOI：10.1016/j.knosys.2011.06.012

• Soft Hierarchical Clustering
• Fuzzy C-Means
• Cluster%26apos
• s quality
• Document clustering
• Quality measures

In this paper an adaptive hierarchical fuzzy clustering algorithm is presented, named Hierarchical Data Divisive Soft Clustering (H2D-SC). The main novelty of the proposed algorithm is that it is a quality driven algorithm, since it dynamically evaluates a multi-dimensional quality measure of the clusters to drive the generation of the soft hierarchy. Specifically, it generates a hierarchy in which each node is split into a variable number of sub-nodes, determined by an innovative quality assessment of soft clusters, based on the evaluation of multiple dimensions such as the cluster%26apos;s cohesion, its cardinality, its mass, and its fuzziness, as well as the partition%26apos;s entropy. Clusters at the same hierarchical level share a minimum quality value: clusters in the lower levels of the hierarchy have a higher quality: this way more specific clusters (lower level clusters) have a higher quality than more general clusters (upper level clusters). Further, since the algorithm generates a soft partition, a document can belong to several sub-clusters with distinct membership degrees. The proposed algorithm is divisive, and it is based on a combination of a modified bisecting K-Means algorithm with a flat soft clustering algorithm used to partition each node. The paper describes the algorithm and its evaluation on two standard collections.

• 出版日期2012-2