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An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality

Amanpreet Kaur Toor, Amarpreet Singh Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2014
© 2013 by IJAIS Journal
10.5120/ijais14-451136
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  1. Amanpreet Kaur Toor and Amarpreet Singh. Article: An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality. International Journal of Applied Information Systems 7(2):5-9, April 2014. BibTeX

    @article{key:article,
    	author = "Amanpreet Kaur Toor and Amarpreet Singh",
    	title = "Article: An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality",
    	journal = "International Journal of Applied Information Systems",
    	year = 2014,
    	volume = 7,
    	number = 2,
    	pages = "5-9",
    	month = "April",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

The cluster analysis method is one of the critical methods in data mining; this method of clustering algorithm will manipulate the clustering results directly. This paper proposes an Advanced Clustering Algorithm in order to addresses the concern of high dimensionality and large data set [1]. The Advanced Clustering Algorithm method avoids computing the distance of each data object to the cluster recursively and save the execution time. ACA requires a simple data structure to store information in each iteration, which is to be used in the next iteration. Experimental results show that the Advanced Clustering Algorithm method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the traditional algorithm Kohonen SOM. This paper includes Advanced Clustering Algorithm (ACA) and its simulated experimental results with different data sets.

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Keywords

ACA, SOM, Clustering, Large Data Set, High Dimensionality, Cluster Analysis