Abstract
We propose a new methodology for clustering data comprising multiple domains or parts, in such a way that the separate domains mutually supervise each other within a semi-supervised learning framework. Unlike existing uses of semi-supervised learning, our methodology does not assume the presence of labels from part of the data, but rather, each of the different domains of the data separately undergoes an unsupervised learning process, while sending and receiving supervised information in the form of data constraints to/from the other domains. The entire process is an alternation of semi-supervised learning stages on the different data domains, based on Basu et al.’s Hidden Markov Random Fields (HMRF) variation of the K-means algorithm for semi-supervised clustering that combines the constraint-based and distance-based approaches in a unified model. Our experiments demonstrate a successful mutual semi-supervision between the different domains during clustering, that is superior to the traditional heterogeneous domain clustering baselines consisting of converting the domains to a single domain or clustering each of the domains separately.
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Abdullin, A., Nasraoui, O.: A semi-supervised learning framework to cluster mixed data types. In: Proceedings of KDIR 2012 - International Conference on Knowledge Discovery and Information Retrieval (2012)
Al-Razgan, M., Domeniconi, C.: Weighted clustering ensembles. In: Proc. of the 6th SIAM ICML (2006)
Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: Proc. of 19th ICML (2002)
Basu, S., Bilenko, M., Mooney, R.J.: A probabilistic framework for semi-supervised clustering. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 59–68 (2004)
Cohn, D., Caruana, R., Mccallum, A.: Semi-supervised clustering with user feedback. Tech. rep. (2003)
Dhillon, I.S., Modha, D.S.: Concept decompositions for large sparse text data using clustering. Mach. Learn. 42, 143–175 (2001)
Ester, M., Peter Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. of the Second International Conference on KDD, pp. 226–231 (1996)
Frank, A., Asuncion, A.: UCI machine learning repository (2010)
Ganti, V., Gehrke, J., Ramakrishnan, R.: Cactus - clustering categorical data using summaries. In: Proc. of the 5th ACM SIGKDD International Conference on KDD, pp. 73–83 (1999)
Ghaemi, R., Sulaiman, M.N., Ibrahim, H., Mustapha, N.: A survey: Clustering ensembles techniques (2009)
Guha, S., Rastogi, R., Shim, K.: Rock: A robust clustering algorithm for categorical attributes. Information Systems 25, 345–366 (2000)
Huang, Z.: A fast clustering algorithm to cluster very large categorical data sets in data mining. In: Research Issues on KDD, pp. 1–8 (1997)
Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery 2, 283–304 (1998)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc. of the 5th Berkeley Symposium on Math. Statistics and Probability, vol. 1, pp. 281–297 (1967)
Plant, C., Böhm, C.: Inconco: interpretable clustering of numerical and categorical objects. In: Proc. of the 17th ACM SIGKDD International Conference on KDD, pp. 1127–1135 (2011)
Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Strehl, A., Strehl, E., Ghosh, J., Mooney, R.: Impact of similarity measures on web-page clustering. In: Workshop on AI for Web Search, pp. 58–64 (2000)
Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. In: Proc. of the 18th ICML, pp. 577–584 (2001)
Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.: Distance metric learning, with application to clustering with side-information. In: Advances in Neural Information Processing Systems 15, pp. 505–512. MIT Press (2002)
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Abdullin, A., Nasraoui, O. (2012). Clustering Heterogeneous Data with Mutual Semi-supervision. In: Calderón-Benavides, L., González-Caro, C., Chávez, E., Ziviani, N. (eds) String Processing and Information Retrieval. SPIRE 2012. Lecture Notes in Computer Science, vol 7608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34109-0_4
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DOI: https://doi.org/10.1007/978-3-642-34109-0_4
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