Abstract
One of the challenges of big data is to combine results of data mining obtained from a distributed dataset. The objective is to minimize the amount data transfer with minimum information loss. A generic combination process will not necessarily provide an optimal ensemble of results. In this paper, we describe a rough clustering problem that leads to a natural ordering of clusters. These ordered rough clusterings are then combined while preserving the properties of rough clustering. A time series dataset of commodity prices is clustered using two different representations to demonstrate the ordered rough clustering process. The information from the ordering of clusters is shown to help us retain salient aspects of individual rough clustering schemes.
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References
Black, F., Scholes, M.: The pricing of options and corporate liabilities. J. Polit. Econ. 81(3), 637–654 (1973)
Fern, X.Z., Lin, W.: Cluster ensemble selection. Stat. Anal. Data Mining 1(3), 128–141 (2008)
Fred, A.: Finding consistent clusters in data partitions. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, p. 309. Springer, Heidelberg (2001)
Gao, C., Pedrycz, W., Miao, D.: Rough subspace-based clustering ensemble for categorical data. Soft Comput. 5(3), 1–16 (2013)
Ghosh, J., Acharya, A.: Cluster ensembles. Wiley Interdisc. Rev. Data Mining Knowl. Discov. 1(4), 305–315 (2011)
Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Trans. Knowl. Discov. Data 1(1), 4 (2007)
Jia, Z., Han, J.: An improved model of executive stock option based on rough set and support vector machines. In: 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008, 1, pp. 256–261. IEEE (2008)
Joshi, M., Lingras, P.: Evolutionary and iterative crisp and rough clustering II: experiments. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds.) PReMI 2009. LNCS, vol. 5909, pp. 621–627. Springer, Heidelberg (2009). http://dx.doi.org/10.1007/978-3-642-11164-8
Lingras, P., Haider, F.: Rough ensemble clustering. In: Intelligent Data Analysis, Special Issue on Business Analytics in Finance and Industry (2014)
Lingras, P., West, C.: Interval set clustering of web users with rough k-means. J. Intell. Inf. Sys. 23(1), 5–16 (2004)
Mitra, S.: An evolutionary rough partitive clustering. Pattern Recogn. Lett. 25(12), 1439–1449 (2004)
Nair, B.B., Mohandas, V., Sakthivel, N.: A decision treerough set hybrid system for stock market trend prediction. Int. J. Comput. Appl. 6(9), 1–6 (2010)
Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)
Pawlak, Z.: Fuzzy Logic for the Management of Uncertainty. Rough sets: a new approach to vagueness. Wiley, Newyork (1992)
Peters, G.: Some refinements of rough k-means clustering. Pattern Recogn. 39(8), 1481–1491 (2006)
Peters, G., Crespo, F., Lingras, P., Weber, R.: Soft clustering-fuzzy and rough approaches and their extensions and derivatives. Int. J. Approximate Reasoning 54(2), 307–322 (2013)
Polkowski, L., Skowron, A.: Rough mereology: a new paradigm for approximate reasoning. Int. J. Approximate Reasoning 15(4), 333–365 (1996)
Skowron, A., Stepaniuk, J.: Information granules in distributed environment. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 357–366. Springer, Heidelberg (1999)
Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003)
Vega-Pons, S., Ruiz-Shulcloper, J.: A survey of clustering ensemble algorithms. Int. J. Pattern Recogn. Artif. Intell. 25(03), 337–372 (2011)
Yao, J., Herbert, J.P.: Financial time-series analysis with rough sets. Appl. Soft Comput. 9(3), 1000–1007 (2009)
Yao, Y., Li, X., Lin, T., Liu, Q.: Representation and classification of rough set models. In: Proceeding of Third International Workshop on Rough Sets and Soft Computing. pp. 630–637 (1994)
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Lingras, P., Haider, F. (2015). Combining Rough Clustering Schemes as a Rough Ensemble. In: Ciucci, D., Wang, G., Mitra, S., Wu, WZ. (eds) Rough Sets and Knowledge Technology. RSKT 2015. Lecture Notes in Computer Science(), vol 9436. Springer, Cham. https://doi.org/10.1007/978-3-319-25754-9_34
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DOI: https://doi.org/10.1007/978-3-319-25754-9_34
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