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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann, San Francisco
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
Aggarwal CC, Reddy CK (2013) Data clustering: algorithm and applications. Chapman & Hall/CRC
Ware VS, Bharathi HN (2013) Study of density based algorithms. Int J Comput Appl 69(26):1–4
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
Suganya M, Nagarajan S (2015) Message passing in clusters using fuzzy density based clustering. Indian J Sci Technol. 8(16):1–6
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
Guo C, Zang Y (2012) Clustering algorithm based on density function and nichePSO. J Syst Eng Electron 23(3):445–452
Sree KS (2014) SSM-DENCLUE: enhanced approach for clustering of sequential data: experiments and test cases. Int J Comput Appl 96:7–13
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
Yan J, Yuan H, Shu X, Zhong S (2009) Optimal clustering algorithm for crime spatial aggregation states analysis. J Tsinghua Univ 49(2)
Yu X, Yu X (2010) On unsupervised clustering algorithm based on distance and density. Comput Appl Softw 27(7)
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
Aggarwal CC (2013) Outlier analysis. Data mining. pp 237–263
Zhou K, Yang S, Ding S, Luo H (2014) On cluster validation. Syst Eng-Theory Pract 34(9)
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
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-10-2335-4_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2334-7
Online ISBN: 978-981-10-2335-4
eBook Packages: Computer ScienceComputer Science (R0)