Document Clustering using K-Medoids

TL;DR

This paper explores the use of the K-Medoids clustering algorithm to organize and summarize documents, aiming to improve data retrieval accuracy in large datasets.

Contribution

It applies the K-Medoids algorithm specifically for document clustering and summarization, which is a novel approach in this context.

Findings

K-Medoids effectively clusters documents.

Clustering improves data retrieval accuracy.

The method enhances document summarization.

Abstract

People are always in search of matters for which they are prone to use internet, but again it has huge assemblage of data due to which it becomes difficult for the reader to get the most accurate data. To make it easier for people to gather accurate data, similar information has to be clustered at one place. There are many algorithms used for clustering of relevant information in one platform. In this paper, K-Medoids clustering algorithm has been employed for formation of clusters which is further used for document summarization.