Hierarchical document clustering based on cosine similarity measure

Clustering is one of the prime topics in data mining. Clustering partitions the data and classifies the data into meaningful subgroups. Document clustering is a set of the document into groups such that two groups show different characteristics with respect to likeness. In this paper, an experimental exploration of similarity based method, HSC for measuring the similarity between data objects particularly text documents is introduced. It also provides an algorithm which has an incremental approach and evaluates cluster likeness between documents that leads to much improved results over other traditional methods. It also focuses on the selection of appropriate similarity measure for analyzing similarity between the documents.