Download Fuzzy Clustering of Web Documents Using Equivalence Relations

Fuzzy Clustering of Web Documents Using
Equivalence Relations and Fuzzy
Hierarchical Clustering
Neha Arora, Devendra Kumar
Department of Computer Science and Engineering
IFTM University
Moradabad
Abstract—WWW is a fertile area for data mining research,[1] as huge amount of information is available in the form of
unstructured and semi structured text databases[2] .It becomes typical to mine the relevant content or information from the
web. So method of document clustering has been introduced as a methodology for improving document retrieval process.
Clustering is a useful method for the textual data mining. Traditional clustering technique uses hard clustering algorithm in
which each document use to belong to only one and exactly one cluster which creates problem to detect multiple themes of the
documents. Clustering can be considered the most important unsupervised learning process which deals with finding the
clusters according to logical relationship or consumer preferences. A cluster can be a structure in a collection of unlabeled
data. The analysis of clusters deals with organizing the data objects into various clusters which has least inter cluster
similarity and more intra cluster similarity [4]. Many clustering algorithms have been proposed by researchers. Partitioning
clustering and hierarchical clustering are two main approaches to clustering. This paper summarizes the agglomerative
hierarchical clustering method and presenting the clusters in the form of a dendrogram. Then Birch multiphase hierarchical
clustering is applied in which clustering features are measured using clustering feature tree.