Abstract
This paper presents a subspace clustering method using an evolutionary-based multi-objective optimization framework. Recently, subspace clustering techniques become popular in solving many clustering problems where the key task is to identify groups of objects where the objects in each group have some similar properties with respect to a subset of features which are relevant to the group. Again, the simultaneous optimization of multiple objective functions helps to identify the subspace clusters effectively. The proposed method optimizes multiple objective functions simultaneously so that it can generate good quality subspace clusters. Two cluster validity indices namely XB-index and PBM-index are modified to make them applicable for subspace clustering problem. The evolutionary-based technique is used to simultaneously optimize these two validity indices to generate the subspace clusters. Various mutation operators have been used to generate good offsprings and to explore the search space effectively. The proposed approach is tested on 7 real-life data sets and 16 synthetic data sets. The efficacy of the proposed method is shown by comparing the results with many state-of-the-art algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications, vol. 27. ACM (1998)
Kailing, K., Kriegel, H.P., Kröger, P.: Density-connected subspace clustering for high-dimensional data. In: Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 246–256. SIAM (2004)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol. 96, pp. 226–231 (1996)
Procopiuc, C.M., Jones, M., Agarwal, P.K., Murali, T.: A Monte Carlo algorithm for fast projective clustering. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, pp. 418–427. ACM (2002)
Yiu, M.L., Mamoulis, N.: Frequent-pattern based iterative projected clustering. In: Third IEEE International Conference on Data Mining, pp. 689–692. IEEE (2003)
Peignier, S., Rigotti, C., Beslon, G.: Subspace clustering using evolvable genome structure. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 575–582. ACM (2015)
Peignier, S.: Subspace clustering on static datasets and dynamic data streams using bio-inspired algorithms. Ph.D. thesis, Université de Lyon; INSA Lyon (2017)
Bandyopadhyay, S., Saha, S., Maulik, U., Deb, K.: A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans. Evol. Comput. 12(3), 269–283 (2008)
Crombach, A., Hogeweg, P.: Chromosome rearrangements and the evolution of genome structuring and adaptability. Mol. Biol. Evol. 24(5), 1130–1139 (2007)
Knibbe, C., Coulon, A., Mazet, O., Fayard, J.M., Beslon, G.: A long-term evolutionary pressure on the amount of noncoding dna. Mol. Biol. Evol. 24(10), 2344–2353 (2007)
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 841–847 (1991)
Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recognit. 37(3), 487–501 (2004)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)
Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Müller, E., Günnemann, S., Assent, I., Seidl, T.: Evaluating clustering in subspace projections of high dimensional data. Proc. VLDB Endow. 2(1), 1270–1281 (2009)
Patrikainen, A., Meila, M.: Comparing subspace clusterings. IEEE Trans. Knowl. Data Eng. 18(7), 902–916 (2006)
Acknowledgement
Dr. Sriparna Saha would like to acknowledge the support of Early Career Research Award of Science and Engineering Research Board (SERB) of Department of Science and Technology India to carry out this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Paul, D., Kumar, A., Saha, S., Mathew, J. (2019). Improved Multi-objective Evolutionary Subspace Clustering. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11953. Springer, Cham. https://doi.org/10.1007/978-3-030-36708-4_57
Download citation
DOI: https://doi.org/10.1007/978-3-030-36708-4_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36707-7
Online ISBN: 978-3-030-36708-4
eBook Packages: Computer ScienceComputer Science (R0)