NDLI: An efficient k-means algorithm integrated with Jaccard distance measure for document clustering

Content Provider	IEEE Xplore Digital Library
Author	Shameem, M.-U.-S. Ferdous, R.
Copyright Year	2009
Description	Author affiliation: Master in Computer Science, University of Trento, Trento, Italy (Ferdous, R.) \|\| Dept. of Computer Science and Engineering, University of Development Alternative (UODA), Dhaka, Bangladesh (Shameem, M.-U.-S.)
Abstract	Document Clustering is a widely studied problem in Text Categorization. It is the process of partitioning or grouping a given set of documents into disjoint clusters where documents in the same cluster are similar. K-means, one of the simplest unsupervised learning algorithms, solves the well known clustering problem following a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. This clustering algorithm uses an iterative procedure which converges to one of numerous local minima. We have found that these iterative techniques are especially sensitive to initial starting conditions of the centroid of each cluster and the more the distance among the cluster centroid the better the clustering performance. In simple K-means algorithm the way to initialize the centroid is not specified and one popular way to start is to randomly choose k points of the samples as k centroids but this process does not guarantee to choose the maximum dissimilar documents as the centroid point for k-cluster. In this paper we proposed a modified k-means algorithm which uses Jaccard distance measure for computing the most dissimilar k documents as centroids for k clusters. Our experimental results demonstrate that our proposed K-means algorithm with Jaccard distance measure for computing the centroid improves the clustering performance of the simple K-means algorithm.
Starting Page	1
Ending Page	6
File Size	584399
Page Count	6
File Format	PDF
ISBN	9781424445691
DOI	10.1109/AHICI.2009.5340335
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-03
Publisher Place	Nepal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	K-Means algorithm Information retrieval Entropy Partitioning algorithms Unsupervised learning Computer science Text categorization F1-Measure Clustering algorithms Measurement techniques Iterative algorithms Recall Iterative methods Document Clustering Precission
Content Type	Text
Resource Type	Article

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