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Aggregate distance based clustering using Fibonacci Series -FIBCLUS

Rawat, Rakesh, Nayak, Richi, Li, Yuefeng, & Alsaleh, Slah (2011) Aggregate distance based clustering using Fibonacci Series -FIBCLUS. In Du, X. (Ed.) APWeb 2011, Springer-Verlag Berlin Heidelberg, Beijing, China, pp. 29-40.

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Abstract

This paper proposes an innovative instance similarity based evaluation metric that reduces the search map for clustering to be performed. An aggregate global score is calculated for each instance using the novel idea of Fibonacci series. The use of Fibonacci numbers is able to separate the instances effectively and, in hence, the intra-cluster similarity is increased and the inter-cluster similarity is decreased during clustering. The proposed FIBCLUS algorithm is able to handle datasets with numerical, categorical and a mix of both types of attributes. Results obtained with FIBCLUS are compared with the results of existing algorithms such as k-means, x-means expected maximization and hierarchical algorithms that are widely used to cluster numeric, categorical and mix data types. Empirical analysis shows that FIBCLUS is able to produce better clustering solutions in terms of entropy, purity and F-score in comparison to the above described existing algorithms.

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ID Code: 47474
Item Type: Conference Paper
Keywords: Clustering, numeric Darasets, categorical and mix datasets, Fibonacci series and golden ratio, Similarity Evaluation
DOI: 10.1007/978-3-642-20291-9_6
ISBN: 978-3-642-20290-2
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Research Centres > Smart Services CRC
Copyright Owner: Copyright 2011 Springer
Copyright Statement: This is the author-version of the work. Conference proceedings published, by Springer Verlag, will be available via SpringerLink http://www.springer.de/comp/lncs/
Deposited On: 05 Dec 2011 11:42
Last Modified: 23 Jul 2014 20:20

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