An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality
Amanpreet Kaur Toor and Amarpreet Singh*
Amritsar College of Engineering & Technology, Punjab, India
- *Corresponding Author:
- Amarpreet Singh
Associate Professor, Amritsar College of Engineering & Technology Manawala
Amritsar, Punjab, India
Tel: 0183-506- 9532
E-mail: [email protected]
Received date: March 29, 2014; Accepted date: April 16, 2014; Published date: April 19, 2014
Citation: Toor AK, Singh A (2014) An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality. J Comput Sci Syst Biol 7:115-118. doi:10.4172/jcsb.1000146
Copyright: © 2014 Toor AK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Cluster analysis method is one of the main analytical methods in data mining; this method of clustering algorithm will influence the clustering results directly. This paper proposes an Advanced Clustering Algorithm in order to solve the question of high dimensionality and large data set. The Advanced Clustering Algorithm method avoids computing the distance of each data object to the cluster centers again and again and save the running time. ACA requires a simple data structure to store information in every 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 algorithms (K-Means, SOM and HAC). This paper includes Advanced Clustering Algorithm (ACA) and its experimental results through experimenting with academic data sets.