An Improved Clustering Algorithm Based on Density Distribution Function

Jianhao Tan, Jing Zhang, Weixiong Li

Abstract


Some characteristics and week points of traditional density-based clustering algorithms are deeply analysed , then an improved way based on density distribution function is put forward. K Nearest Neighbor( KNN ) is used to measure the density of each point, then a local maximum density point is defined as the center point.. By means of local scale, classification is extended from the center point. For each point there is a procedure to find whether it is a core point by a radius scale factor. Then the classification is extended once again from the core point until the density descends to the given ratio of the density of the center point. The tests show that the improved algorithm greatly improves the sensitivity of density-based clustering algorithms to parameters and enhances the clustering effect of the high-dimensional data sets with uneven density distribution. 


Full Text: PDF DOI: 10.5539/cis.v3n3p23

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This work is licensed under a Creative Commons Attribution 3.0 License.

Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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