DDG-Clustering: A novel technique for highly accurate results

Zahraa S. Abdallah, Mohamed Medhat Gaber

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings of the IADIS European Conference on Data Mining 2009 (ECDM'09)
Publication statusPublished - 2009

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