Adaptive K-Means Algorithm with Dynamically Changing Cluster Centers and K-Value

Ai Ping Deng; Ben Xiao; Hui Yong Yuan

doi:10.4028/www.scientific.net/AMR.532-533.1373

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533Adaptive K-Means Algorithm with Dynamically...

Adaptive K-Means Algorithm with Dynamically Changing Cluster Centers and K-Value

Abstract:

In allusion to the disadvantage of having to obtain the number of clusters in advance and the sensitivity to selecting initial clustering centers in the K-means algorithm, an improved K-means algorithm is proposed, that the cluster centers and the number of clusters are dynamically changing. The new algorithm determines the cluster centers by calculating the density of data points and shared nearest neighbor similarity, and controls the clustering categories by using the average shared nearest neighbor self-similarity.The experimental results of IRIS testing data set show that the algorithm can select the cluster cennters and can distinguish between different types of cluster efficiently.

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Periodical:

Advanced Materials Research (Volumes 532-533)

Pages:

1373-1377

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.532-533.1373

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Online since:

June 2012

Authors:

Ai Ping Deng, Ben Xiao, Hui Yong Yuan

Keywords:

Density, K-Means Algorithm, Nearest Neighbor Similarity, Self-Similarity

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References

[1] Lai Yuxia, Liu Jianping. K-means algorithm for optimization of the initial cluster centers [J]. Computer Engineering and Applications, 2008, 44 (10) : 147-149.

Google Scholar

[2] Yuan Fang, Zhou Zhuan Yong, Ning Xin. The initial cluster centers for k-means algorithm for optimization [J]. Computer Engineering, 2007, 33 (3) : 65-66.

Google Scholar

[3] Wang Zhong, Liu Guiquan, CHEN Enhong. The K-means algorithm for an optimized initial center point [J]. Pattern Recognition and Artificial Intelligence, 2009, 22 (2) : 299-304.

Google Scholar

[4] TAN Pangning, STEINBACH M, KUMAR V. Introduction to Data Mining [M]. Beijing: People's Posts and Telecommunications Press, (2006).

Google Scholar

[5] Liu Suqin, Chai Song. Dynamic test method of k-means for Named entity network topics [J]. Intelligent Systems, 2010, 5 (2) : 122-16.

Google Scholar

[6] Steinbach M, Karypis G, kumar V. A comparison of document clustering techniques. In: Proceedings of KDD Workshop on Text mining. Boston, MA, USA: ACM Press, (2000).

Google Scholar

[7] Qin Yu, Jing Jiwu, Xiang Ji, Zhang Aihua. The k-means Algorithm based on optimization of the initial class center point [J]. Chinese Academy of Sciences, 2007, 24 (6) : 771-777.

Google Scholar

[8] Shi Peibei, Guo Yutang, Hu Yujuan, Yu Chun. Initialized independent spectral clustering algorithm [J]. Computer Engineering and Applications, 2010, 46 (25) : 134-137.

Google Scholar