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An Anonymous Algorithm for Calculating Dissimilarity Metric on Clustering

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DOI: 10.23977/cpcs.2016.11004 | Downloads: 38 | Views: 4449

Author(s)

Sun Yi 1, Li Chi 1

Affiliation(s)

1 Beijing University of Posts and Telecommunications, China

Corresponding Author

Sun Yi

ABSTRACT

By the research on calculating the dissimilarity metric among tuples with many different attributes based on clustering, this paper improves dissimilarity metric algorithm, which can more accurately reflect the differences between tuples. Besides, in terms of various attribute types ,the value of attribute is divided into multi-category. According to the multi-category, we come to the final dissimilarity metric result through analysis. The experimental results show that this algorithm is able to achieve highly accurate dissimilarity metric results.

KEYWORDS

dissimilarity metric; multi-category; interval-scaled attribute; binary attribute; nominal attribute; ordinal attribute; proportional-scaled attribute; symmetric binary attribute; asymmetric binary attribute; clustering

CITE THIS PAPER

Chi, L. and Sun, Y. (2016) An Anonymous Algorithm for Calculating Dissimilarity Metric on Clustering. Computing, Performance and Communication systems (2016) 1: 22-27.

REFERENCES

[1] Ming Li, Xin Chen, Xin Li, Bin Ma, and Paul M.B.Vitanyi. IEEE Transactions on Information Theory, December 2004, Vol. 50. No. 12.  
[2] Jiawei Han, Micheline Kamber, Jian Pei, Data Mining Concepts and Techniques Third Edition, 2012: 65-73
[3] Zirong Yang, Research on domain oriented high performance information retrieval based on Web data mining
[4] Thomas Junk, Confidence level computation for combining searches with small statistics, Nuclear Instruments and Methods in Physics Research Section A:Accelerators, Spectrometers, Detectors and Associated Equipment, 21 September 1999, Volume 434, Issues 2-3, pp. 435-443. 
[5] Yu Fang, Zhong-Hui Liu, Fan Min, Multi-objective cost-sensitive attribute reduction on data with error ranges, International Journal of Machine Learning and Cybernetics, October 2016, Volume 7, Issue 5, pp. 783-793. 

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