Fuzzy clustering of web documents using equivalence relations and fuzzy hierarchical clustering

TL;DR

This paper introduces a fuzzy clustering algorithm for web documents that handles vagueness and uncertainty by allowing objects to belong to multiple clusters with varying degrees of membership, using equivalence relations and hierarchical methods.

Contribution

It presents a novel fuzzy hierarchical clustering algorithm specifically designed for web documents, incorporating equivalence relations to better manage uncertain boundaries.

Findings

Effective clustering of web documents with fuzzy boundaries

Demonstrated the algorithm's ability to handle uncertain data

Experimental results show improved clustering performance

Abstract

The conventional clustering algorithms have difficulties in handling the challenges posed by the collection of natural data which is often vague and uncertain. Fuzzy clustering methods have the potential to manage such situations efficiently. Fuzzy clustering method is offered to construct clusters with uncertain boundaries and allows that one object belongs to one or more clusters with some membership degree. In this paper, an algorithm and experimental results are presented for fuzzy clustering of web documents using equivalence relations and fuzzy hierarchical clustering.