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

Aman Toor

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

Aman Toor *

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

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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.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Aman Toor. 2014. "An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C2).

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GJCST Volume 14 Issue C2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

ACA Cluster analysis Clustering HAC high dimensionality K-means large data set SOM

Classification

GJCST-C Classification H.3.3

Submission ReceivedDecember 6, 2013
Peer Review Double Blind
Handling Editor
Accepted January 4, 2014
Published January 15, 2014

Version of record

v1.2

Issue date

May 15, 2014

Language

English

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Article Matrices

Total Score: 141

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Aman Toor (PhD/Dr. count: 0)

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Publish Date: 2014 01, Wed

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