Abstract
In the face of a growing number of large-scale data sets, affinity propagation clustering algorithm to calculate the process required to build the similarity matrix, will bring huge storage and computation. Therefore, this paper proposes an improved affinity propagation clustering algorithm. First, add the subtraction clustering, using the density value of the data points to obtain the point of initial clusters. Then, calculate the similarity distance between the initial cluster points, and reference the idea of semi-supervised clustering, adding pairs restriction information, structure sparse similarity matrix. Finally, the cluster representative points conduct AP clustering until a suitable cluster division. Experimental results show that the algorithm allows the calculation is greatly reduced, the similarity matrix storage capacity is also reduced, and better than the original algorithm on the clustering effect and processing speed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Demiriz, A., Benneit, K.P., Embrechts, M.J.: Semi-supervised clustering using genetic algorithm. In: Proc of Intelligent Engineering systems through Artificial Neural Networks, pp. 809–814 (1999)
Liu, X., Yin, M., Luo, J.: An Improved Affinity Propagation Clustering Algorithm for Large-scale Data Sets. In: 2013 Ninth International Conference on Natural Computation, pp. 894–899 (2013)
Zhang, X., Furtlehner, C., Germain-Renaud, C., Sebag, M.: Data Stream Clustering with Affinity Propagation. IEEE Transactions on Knowledge and Data Engineering 26(7), 1644–1656 (2014)
Frey, B.J., Dueck, D.: clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Fu, Y.-D., Lan, J.-L.: Kernel-based adaptation for affinity propagation clustering algorithm. Application Research of Computers 29(5), 1644–1647 (2012)
Li, X., Wang, L., Song, Y.: Parallel computation of semi-supervised clustering algorithm based on affinity propagation. Computer Engineering and Applications 47(7), 149–152 (2011)
Feng, X.-L., Yu, H.-T.: Semi-supervised affinity propagation clustering based on manifold distance. Computer Engineering and Applications 28(10), 3656–3658 (2011)
Jun, D., Suo-Ping, W., Fan-Lun, X.: Affinity Propagation Clustering Based on Variable-Similarity Measure. Journal of Electronics & Information Technology 32(3), 509–514 (2010)
Chiu, S.L.: Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems 2(3), 267–278 (1994)
Cai, W., Cheng, J.: Fuzzy Clustering Based on Subtractive Clustering. Lanzhou Jiao Tong University Learned Journal 30(6), 50–54 (2011)
Nikhil, R.P., Chakraborty, D.: Mountain and subtractive clustering method: Improvements and generalizations [J]. International Journal of Intelligent Systems 15(4), 329–341 (2000)
Dash, M., Huan, L., Scheuermann, P., TanK, L.: Fast hierarchical clustering and its validation. Data & Knowledge Engineering 44, 109–138 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, Q., Zhang, H., Yang, Q. (2015). Semi-supervised Affinity Propagation Clustering Based on Subtractive Clustering for Large-Scale Data Sets. In: Wang, H., et al. Intelligent Computation in Big Data Era. ICYCSEE 2015. Communications in Computer and Information Science, vol 503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46248-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-662-46248-5_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46247-8
Online ISBN: 978-3-662-46248-5
eBook Packages: Computer ScienceComputer Science (R0)