Influence Analysis of Linear Data Distribution on Different Clustering Results

Abstract:

Through simulating kinds of practical data distribution forms, we could construct different linear data distribution based on three-dimensional space. According to the widely applicable clustering methods based on traditional methods classification, we could compare and analysis of different clustering results on kinds of linear distribution data. Firstly, we could conduct the clustering experiments of simple linear distribution data; secondly, according to the experience above, we could conduct the clustering experiments of complex linear distribution data. At the same time, combining mature computer visualization technology of displaying clustering results with the widely applicable evaluation index of interpretative statement, we could come to the conclusion that relevant influences of linear data distribution on clustering results of different clustering methods, proposing more optimized clustering analysis process on different linear data distributions.

• Periodical: Advanced Materials Research (Volumes 472-475)
• Pages: 3144-3152
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.472-475.3144
• Online since: February 2012
• Authors: Yu Chen Song, Xiao Liang Jia, Hai Dong Meng
• Keywords: Clustering, Clustering Result Visualization, Data Distribution

References

[1] Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining (Beijing: China Machine Press, China 2010).

[2] Yu-Chen Song, Hai-Dong Meng, Clustering Algorithms for Arbitray Data Sets, edtied by Encyclopedia of Artificial Intelligence Idea Group Inc, Information Science Reference, (2010) in press.

[3] Jiawei Han, Micheline Kamber: Data Mining: Concept and Techniques (Beijing: China Machine Press, China 2008).

[4] Yuan Chang-an: Data Mining Theory and SPSS Clementine Application (Beijing: Publishing House of Electronics Industry, China 2009).

[5] Peter J.ROUSSEEUW, Computational and Applied Mathematics 20(1987)53-65, in press.

[6] Richard A. Johnson, Dean W. Wichern: Applied Multivariate Statistical Analysis (Pearson Education International, China 2003).

[7] Roy Gelbard, Orit Goldman, Israel Spiegler, "Investigating diversity of clustering methods: an empirical comparison," Data & Knowledge Engineer 63(2007) 155-166, in press.

DOI: 10.1016/j.datak.2007.01.002

[8] Gaiming Zhong, Duoqian Miao, Ruizhi Wang, "A graph-theoretical clustering method based on two rounds of minimum spanning trees," Pattern Recognition 43(2010) 752-766, in press.

DOI: 10.1016/j.patcog.2009.07.010

[9] Mohamed M. Mostafa, "A Psycho-cognitive segmentation of organ donors in Egypt using Kohonen's self-organizing maps," Expert Systems with Applications 38(2011) 6906-6915, in press.

DOI: 10.1016/j.eswa.2010.12.033

[10] Christophe, Nicolas M, Jean-Pierre, "Arbitrary shaped multiple spatial cluster detection for case event data," Computational Statistics and Data Analysis, (2004), in press.

[11] Andriy Kovalenko, Fumio Hirata, "Three-dimension density profiles of water in contact with a solute of arbitrary shape: a RISM approach," Chemical Physics Letters 290(1998) 237-244, in press.

DOI: 10.1016/s0009-2614(98)00471-0

[12] R. Lleti, M.C. Ortiz, L.A. Sarabia, M.S. Sanchez, "Selecting variables for k-means cluster analysis by using a genetic algorithm that optimizes the silhouettes," Analytica Chimica Acta 515(2004)87-100, in press.

DOI: 10.1016/j.aca.2003.12.020