Abstract
With vast amount of data generated, it is becoming a main aspect to mine useful information from such data. Clustering research is an important task of data mining. Traditional clustering algorithms such as K-means algorithm are too old to propose high-dimensional data, so an efficient clustering algorithm, spectral clustering is generated. In recent years, more and more scholars has been firmly committing to studying spectral clustering algorithm for its solid theoretical foundation and excellent clustering results. In this paper we propose an improved spectral clustering algorithm based on Dynamic Tissue-like P System abbreviated as ISC-DTP. ISC-DTP algorithm takes use of the advantages of maximal parallelism in tissue-like membrane system. Experiment is conducted on an artificial data set and four UCI data sets. And we compare the ISC-DTP algorithm with original spectral clustering algorithm and K-means algorithm. The experiments demonstrate the effectiveness and robustness of the proposed algorithm.
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References
Liu, X., Xue, J.: Spatial cluster analysis by the bin-packing problem and DNA computing technique. Discret. Dyn. Nat. Soc. 5187, 845–850 (2013)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. Adv. Neural. Inf. Process. Syst. 14, 849–856 (2002)
Pǎun, G.: Computing with membranes. J. Comput. Syst. Sci. 61, 108–143 (2000)
Wang, T.-S., Lin, H.-T.: Weighted-spectral clustering algorithm for detecting community structures in complex networks. Artif. Intell. Rev. 47, 463–483 (2017)
Higham, D.J., Kibble, M.: A unified view of spectral clustering. University of Strathclyde Mathematics Research Report 02 (2004)
Pǎun, G.: From cells to computer: computing with membranes. BioSystems 59, 139–158 (2001)
Liu, X., Xue, J.: A cluster splitting technique by Hopfield networks and tissue-like P systems on simplices. Neural Process. Lett. 46, 171–194 (2017)
Liu, X., Zhao, Y., Sun, M.: An improved apriori algorithm based on an evolution-communication tissue-like tissue-like P system with promoters and inhibitors. Discrete Dyn. Nat. Soc. 1, 1–11 (2017)
Le, J., Jiang, T.: Robust K-means algorithm with automatically splitting and merging clusters and its applications for surveillance data. Multimed. Tools Appl. 75, 12043–12059 (2016)
Wang, B., Zhang, L.: Spectral clustering based on similarity and dissimilarity criterion. Pattern Anal. Appl. 20, 495–506 (2017)
Lichman, M.: UCI machine learning repository. University California, School of Information and Computer Science, Irvine, CA, USA (2013). http://archive.ics.uci.edu/ml
Wang, J., Shi, P., Peng, H.: Weighted fuzzy spiking neural P systems. IEEE Trans. Fuzzy Syst. 21(2), 209–220 (2013)
Han, R., Liu, J.: Spectral clustering and genetic algorithm for design of district metered areas in water distribution systems. Procedia Eng. 186, 152–159 (2017)
Acknowledgment
This research project was partly supported by the National Natural Science Foundation of China (61472231, 61502283, 61640201), the Ministry of Education of Humanities and Social Science of China (12YJA630152), and the Shandong Social Science Fund of China (16BGLJ06, 11CGLJ22).
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Hu, X., Liu, X. (2018). An Improved Spectral Clustering Algorithm Based on Dynamic Tissue-Like Membrane System. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_38
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DOI: https://doi.org/10.1007/978-3-030-02698-1_38
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