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

Authors

  • Aman Toor

Keywords:

ACA, SOM, K-MEANS, HAC, clustering, large data set, high dimensionality, cluster analysis

Abstract

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 this question requiring a simple data structure to store some information 1 in every iteration which is to be used in the next iteration The Advanced Clustering Algorithm method avoids computing the distance of each data object to the cluster centers repeat saving the running time 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 This paper includes Advanced Clustering Algorithm ACA and describes the experimental results and conclusions through experimenting with academic data sets

How to Cite

Aman Toor. (2014). An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality. Global Journal of Computer Science and Technology, 14(C2), 71–74. Retrieved from https://computerresearch.org/index.php/computer/article/view/77

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

Published

2014-01-15