ISSN : 2319-7323

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE ENGINEERING

ABSTRACT

Title	:	Analysis of Different Clustering Techniques in Data and Text Mining
Authors	:	Ms.S.Prabha, Dr.K.Duraiswamy, Ms.M.Sharmila
Keywords	:	Clustering; Semi Supervised; Ontology; Semantic; Constraint; Similarity measures;
Issue Date	:	March 2014
Abstract	:	In recent days clustering becomes important in pattern detection, unsupervised learning process, data concept construction, information retrieval, text mining, web analysis, marketing and medical diagnostic. The purpose of this paper is an attempt to reconnoiter some of the important clustering techniques in the data mining literature and to compare some aspects of clustering algorithms which contains performance, order of input, accuracy, scalability, shapes discovered, dimensionality and dealing with noisy data. The algorithms are Partitional approach, hierarchical approach, seeded approach, ontology approach, concept based approach.
Page(s)	:	107-116
ISSN	:	2319-7323
Source	:	Vol. 3, No.2