Clustering is an important tool in data analysis, as
data set grows then their properties and interrelationships will
also change. There are different types of cluster model:
Connectivity models, Distribution models, Centroid models,
Subspace model, Group models and Graph-based models.
Clustering algorithms can be categorized based on the models
which are using .Traditionally clustering techniques are broadly
divided into hierarchical and density based clustering. There are
so many clustering methods because the notion of cluster cannot
be easily defined. Data mining deals with large data sets and
their relationships, while we are imposing clustering to analyze
the huge data that needs additional challenges. This leads to an
efficient and broadly applicable clustering method. In this paper
some of the clustering techniques are discussed.
Published In : IJCAT Journal Volume 1, Issue 4
Date of Publication : 31 May 2014
Pages : 42 - 46
Figures : 03
Tables : 01
Publication Link : IJCAT-2014/1-4/A Comparison of Clustering Techniques in Data
Mining
Rahumath Beevi A : Received the bachelor’s degree in Computer
Science and Engineering from Cochin University of Science and
Technology, Kerala in 2012. Presently she is pursuing her Mtech in
the department of Computer Science and Engineering from Cochin
University of Science and Technology, Kerala. Her research interests
include Data Mining.
Remya R : Received the bachelor’s degree in Information Technology
from University college Trivandrum, Kerala in 2004 and master’s
degree in Computer Science and Engineering from Anna University,
Coimbatore in 2008. Currently working as an Assistant Professor in
Information Technology department, of College of Engineering
Perumon,under Cochin university of Science and Technology. She
has teaching experience of eight years. Research interests Includes
Data Mining.
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