DDG-Clustering: A novel technique for highly accurate results

Zahraa S. Abdallah; Mohamed Medhat Gaber

DDG-Clustering: A novel technique for highly accurate results

Zahraa S. Abdallah, Mohamed Medhat Gaber

Department of Engineering Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

5 Citations (Scopus)

Abstract

A key to the success of any clustering algorithm is the similarity measure applied. The similarity among different instances is defined according to a particular criterion. State-of-the-art clustering techniques have used distance, density and gravity measures. Some have used a combination of two. Distance, Density and Gravity clustering algorithm "DDG-Clustering" is our novel clustering technique based on the integration of three different similarity measures. The basic principle is to combine distance, density and gravitational perspectives for clustering purpose. Experimental results illustrate that the proposed method is very efficient for data clustering with acceptable running time.

Original language	English
Title of host publication	Proceedings of the IADIS European Conference on Data Mining 2009 (ECDM'09)
Publication status	Published - 2009

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title = "DDG-Clustering: A novel technique for highly accurate results",

abstract = "A key to the success of any clustering algorithm is the similarity measure applied. The similarity among different instances is defined according to a particular criterion. State-of-the-art clustering techniques have used distance, density and gravity measures. Some have used a combination of two. Distance, Density and Gravity clustering algorithm {"}DDG-Clustering{"} is our novel clustering technique based on the integration of three different similarity measures. The basic principle is to combine distance, density and gravitational perspectives for clustering purpose. Experimental results illustrate that the proposed method is very efficient for data clustering with acceptable running time.",

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AB - A key to the success of any clustering algorithm is the similarity measure applied. The similarity among different instances is defined according to a particular criterion. State-of-the-art clustering techniques have used distance, density and gravity measures. Some have used a combination of two. Distance, Density and Gravity clustering algorithm "DDG-Clustering" is our novel clustering technique based on the integration of three different similarity measures. The basic principle is to combine distance, density and gravitational perspectives for clustering purpose. Experimental results illustrate that the proposed method is very efficient for data clustering with acceptable running time.

UR - https://research.monash.edu/en/publications/f25d90b9-ff54-4b95-b7ae-49ef8de63388

M3 - Conference Contribution (Conference Proceeding)

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BT - Proceedings of the IADIS European Conference on Data Mining 2009 (ECDM'09)

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