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Analysis of Complete-Link Clustering for Identifying and Visualizing Multi-attribute Transactional Data using MATLAB

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IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication (ETCC-2014)
© 2014 by IJCA Journal
ETCC - Number 1
Year of Publication: 2014
Authors:
Arna Prabha Jena
Annan Naidu Paidi

Arna Prabha Jena and Annan Naidu Paidi. Article: Analysis of Complete-Link Clustering for Identifying and Visualizing Multi-attribute Transactional Data using MATLAB. IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication (ETCC-2014) ETCC(1):44-50, September 2014. Full text available. BibTeX

@article{key:article,
	author = {Arna Prabha Jena and Annan Naidu Paidi},
	title = {Article: Analysis of Complete-Link Clustering for Identifying and Visualizing Multi-attribute Transactional Data using MATLAB},
	journal = {IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication (ETCC-2014)},
	year = {2014},
	volume = {ETCC},
	number = {1},
	pages = {44-50},
	month = {September},
	note = {Full text available}
}

Abstract

In recent years, entirely the data mining has drawn towards a great deal of interest in the field of information industry due to the wide availableness of enormous amount of data and the imminent need for turning such data into useful information and knowledge. Clustering is a powerful field of research in data mining. Many clustering algorithms have been developed to find patterns representing knowledge and are implicitly stored or captured in large databases etc, to provide decision support to the users. The quality of clustering can be assessed based on a metric of dissimilarity of objects, computed for various types of data. This paper presents, one of the agglomerative approaches of hierarchical clustering techniques i. e. complete-linkage clustering by considering four different types of distance metrics using Matlab toolbox, in order to compute distances (similarities/dissimilarities) between the new cluster and each of the old clusters.

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