Text clustering divides a set of texts into clusters such that texts within each cluster are similar in content. It may be used to uncover the structure and content of unknown text sets as well as to give new perspectives on familiar ones. The focus of this paper is to experimentally evaluate the quality of clusters obtained using partitional clustering algorithms that employ different clusteri...

The purpose of this study is evaluation and comparison of some criterion functions used for document clustering. Each function is evaluated by using different clustering methods and different datasets. Detailed experiments show that some clustering criterion functions perform better than rest. Results of experiments are also consistent with previous works which compares same criterion functions.

Parametric, model-based algorithms learn generative models from the data, with each model corresponding to one particular cluster. Accordingly, the model-based partitional algorithm will select the most suitable model for any data object (Clustering step), and will recompute parametric models using data specifically from the corresponding clusters (Maximization step). This Clustering-Maximizati...

Clustering with constraints is an important recent development in the clustering literature. The addition of constraints allows users to incorporate domain expertise into the clustering process by explicitly specifying what are desirable properties in a clustering solution. This is particularly useful for applications in domains where considerable domain expertise already exists. In this first ...

Clustering is an important technique in data mining. It is a technique in which grouping of item taken place into the clusters in such a way that items of same cluster have more similarity than the items into another cluster, but is very dissimilar to the item in other clusters. The aim of document clustering is to make a set of clusters of given documents in such a way that document of each cl...

There is a tremendous spread in the amount of information on the largest shared information source like search engine. Fast and standards quality document clustering algorithms play an important role in helping users effectively towards vertical search engine, World Wide Web, summarizing & organizing information. Recent surveys have shown that partitional clustering algorithms are more suitable...

Three approaches to partitional clustering using genetic algorithms (GA) are compared with k-means and the EM algorithm for three real world datasets (Iris, Glass and Vowel). The GA techniques differ in their encoding of the clustering problem using either a class id for each object (GAIE), medoids to assign objects to the class associated with the nearest medoid (GAME), or parameters for multi...

A problem with the modelling of clusters as d dimensional centroids is that centroids cannot relay much information about cluster shape i.e. elongated, circular, irregular etc... The Agglomerative-Partitional Clustering (APC) methodology introduced here attempts to remedy this situation by joining together centroids coexisting within regions of relatively high density with line segments. Interc...