An Outlier Detection Method Based on Fuzzy C-Means Clustering

Qiang Li; Jian Pei Zhang; Guang Sheng Feng

doi:10.4028/www.scientific.net/KEM.419-420.165

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 419-420An Outlier Detection Method Based on Fuzzy C-Means...

An Outlier Detection Method Based on Fuzzy C-Means Clustering

Abstract:

Both fuzzy c-means (FCM) clustering and outlier detection are useful data mining techniques in real applications. In this paper, we show that the task of outlier detection could be achieved as by-product of fuzzy c-means clustering. The proposed strategy consists of two stages. The first stage consists of purely fuzzy c-means process, while the second stage identifies exceptional objects according to a novel metric based on the entropy of membership values. We provide experimental results to demonstrate the effectiveness of our technique.

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Key Engineering Materials (Volumes 419-420)

Pages:

165-168

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.419-420.165

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Online since:

October 2009

Authors:

Qiang Li, Jian Pei Zhang, Guang Sheng Feng

Keywords:

Clustering, Entropy, Fuzzy C-Means (FCM), Information Theory, Outlier

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References

[1] J Mac Queen. Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 281-297.

Google Scholar

[2] J. C Bezdek. Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York. (1981).

Google Scholar

[3] J. Yu, Q. Cheng, H. Huang: Analysis on weighting exponent in the FCM. IEEE Transactions on SMC, Part B-Cybernetics, 2004, 31: 634-639.

DOI: 10.1109/tsmcb.2003.810951

Google Scholar

[4] E. Knorr, R. Ng, T. Tucakov. Distance-based outliers: Algorithms and applications. VLDB Journal, 2000, 8(3-4): 237-253.

DOI: 10.1007/s007780050006

Google Scholar

[5] F. Angiulli, C. Pizzuti. Outlier Mining in Large High-Dimensional Data Sets. IEEE Transactions on Knowledge and Data Engineering, (2005).

DOI: 10.1109/tkde.2005.31

Google Scholar

[6] S. Papadimitriou, H. Kitagawa, P. B. Gibbons, C. Faloutsos. Fast Outlier Detection Using the Local Correlation Integral. In: Proc 2003 Int. Conf. Data Engineering (ICDE'03), (2003).

DOI: 10.1109/icde.2003.1260802

Google Scholar

[7] Wei Tang, Taghi M. Khoshgoftaar: Noise Identification with the k-Means Algorithm. In: proc. of 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'04), 2004, pp.373-378.

DOI: 10.1109/ictai.2004.93

Google Scholar

[8] S. Harkins, H. He, G. J. Willams, R. A. Baster. Outlier Detection Using Replicator Neural Networks. In: Proc. of 4th International Conference on Data Warehousing and Knowledge Discovery (DaWaK'02), pp.170-180, (2002).

DOI: 10.1007/3-540-46145-0_17

Google Scholar

[9] C. J. Merz, P. Merphy. UCI Repository of Machine Learning Databases, 1996. (http: /www. ics. uci. edu/~mlearn/MLRRepository. html).

Google Scholar