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Title:
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DDG-CLUSTERING: A NOVEL TECHNIQUE FOR HIGHLY ACCURATE RESULTS |
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Author(s):
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Zahraa Said Ammar , Mohamed Medhat Gaber |
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ISBN:
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978-972-8924-88-1 |
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Editors:
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Ajith P. Abraham |
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Year:
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2009 |
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Edition:
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Single |
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Keywords:
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Data mining, data clustering, cluster density, cluster gravity, k-means clustering. |
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Type:
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Short Paper |
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First Page:
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163 |
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Last Page:
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167 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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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 DDGClustering
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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