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A Novel Algorithm Based on Avoid Determining Noise Threshold in DENCLUE

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 405))

Abstract

This paper focuses on density-based clustering analysis. The determination of noise threshold set in DENCLUE is studied via analyzing several typical density-based clustering methods. An improved algorithm which does not use the noise threshold in DENCLUE is proposed based on the estimation of points in inner cluster. Compared to the original DENCLUE, smaller silhouette coefficients can be obtained from the proposed algorithm via experimental verification. Meanwhile, the noise in data sets can also be verified well in our method, which can be viewed as an improvement for applicability and performance of DENCLUE.

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References

  1. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann, San Francisco

    Google Scholar 

  2. Padhy N, Dr. Mishra P, Panigrahi R (2012) Summay of data mining. The survey of data mining applications and feature scope. Int J Comput Sci Eng Informa 2(3):43–58

    Google Scholar 

  3. Aggarwal CC, Reddy CK (2013) Data clustering: algorithm and applications. Chapman & Hall/CRC

    Google Scholar 

  4. Ware VS, Bharathi HN (2013) Study of density based algorithms. Int J Comput Appl 69(26):1–4

    Google Scholar 

  5. Hinneburg A, Gabriel H-H (2007) DENCLUE 2.0: fast clustering based on kernel density estimation. In: International symposium on advances in intelligent data analysis. vol 4723, pp 70–80

    Google Scholar 

  6. Suganya M, Nagarajan S (2015) Message passing in clusters using fuzzy density based clustering. Indian J Sci Technol. 8(16):1–6

    Article  Google Scholar 

  7. Chang DX, Zhang XD, Zheng CW, Zhang DM (2010) A robust dynamic niching genetic algorithm with niche migration for automatic clustering problem. Pattern Recogn 43(4):1346–1360

    Article  MATH  Google Scholar 

  8. Guo C, Zang Y (2012) Clustering algorithm based on density function and nichePSO. J Syst Eng Electron 23(3):445–452

    Article  Google Scholar 

  9. Sree KS (2014) SSM-DENCLUE: enhanced approach for clustering of sequential data: experiments and test cases. Int J Comput Appl 96:7–13

    Google Scholar 

  10. Liang Z, D L, Fei H, Yifei T, Yanqiang Yuan (2015) Fault disgnosis of belt weigher using the improved DENCLUE and SVM. Harbin Gongye Daxue Xuebao/J Harbin Institute Technol 47(7):122–128

    Google Scholar 

  11. Yan J, Yuan H, Shu X, Zhong S (2009) Optimal clustering algorithm for crime spatial aggregation states analysis. J Tsinghua Univ 49(2)

    Google Scholar 

  12. Yu X, Yu X (2010) On unsupervised clustering algorithm based on distance and density. Comput Appl Softw 27(7)

    Google Scholar 

  13. Hinneburg A, Keim DA (1998) An efficient approach to clustering in large multimedia databases with noise. In: Proceedings of the 4th international conference on knowledge discovery and data mining. AAAI Press, New York, pp 58–65

    Google Scholar 

  14. Aggarwal CC (2013) Outlier analysis. Data mining. pp 237–263

    Google Scholar 

  15. Zhou K, Yang S, Ding S, Luo H (2014) On cluster validation. Syst Eng-Theory Pract 34(9)

    Google Scholar 

  16. Liu Y, Li Z, Xiong H, Sen W (2013) Understanding and enhancement of internal clustering validation measures. IEEE Trans Syst Man Cybern Part B Cybern A Publ IEEE Syst Man Cybern Soc 43(3):982–994

    Google Scholar 

Download references

Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant 61203084 and 61374135, Basic Science and Advanced Technology Research Projects of Chongqing under Grant cstc2015jcyjA0480, and Chongqing University Postgraduates Innovation Project (CYB15051).

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Correspondence to Ke Zhang .

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© 2016 Springer Science+Business Media Singapore

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Zhang, K., Xiong, Y., Huang, L., Chai, Y. (2016). A Novel Algorithm Based on Avoid Determining Noise Threshold in DENCLUE. In: Jia, Y., Du, J., Zhang, W., Li, H. (eds) Proceedings of 2016 Chinese Intelligent Systems Conference. CISC 2016. Lecture Notes in Electrical Engineering, vol 405. Springer, Singapore. https://doi.org/10.1007/978-981-10-2335-4_29

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  • DOI: https://doi.org/10.1007/978-981-10-2335-4_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2334-7

  • Online ISBN: 978-981-10-2335-4

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