Abstract
Ryszard Michalski has been the pioneer of Machine Learning. His conceptual clustering focused on the understandability of clustering results. It is a key requirement if Machine Learning is to serve users successfully. In this chapter, we present two approaches to clustering in the scenario of Web 2.0 with a special concern of understandability in this new context. In contrast to semantic web approaches which advocate ontologies as a common semantics for homogeneous user groups, Web 2.0 aims at supporting heterogeneous user groups where users annotate and organize their content without a reference to a common schema. Hence, the semantics is not made explicit. It can be extracted by Machine Learning, though, hence providing users with new services.
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References
Beil, F., Ester, M., Xu, X.: Frequent term-based text clustering. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, KDD (2002)
Coello Coello, C.A.: A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems 1(3), 129–156 (1999)
Datta, S., Bhaduri, K., Giannella, C., Wolff, R., Kargupta, H.: Distributed data mining in peer-to-peer networks. IEEE Internet Computing, special issue on distributed data mining (2005)
Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, Springer, Heidelberg (2000)
Dhillon, I.S., Guan, Y., Kulis, B.: Kernel k-means: spectral clustering and normalized cuts. In: Proc. of the conference on Knowledge Discovery and Data Mining (2004)
Flasch, O., Kaspari, A., Morik, K., Wurst, M.: Aspect-based tagging for collaborative media organization. In: Berendt, B., Hotho, A., Mladenic, D., Semeraro, G. (eds.) WebMine 2007. LNCS (LNAI), vol. 4737, pp. 122–141. Springer, Heidelberg (2007)
Fung, B.C.M., Wang, K., Ester, M.: Hierarchical document clustering using frequent itemsets. In: Proceedings of the SIAM International Conference on Data Mining (2003)
Guha, S., Rastogi, R., Shim, K.: CURE: an efficient clustering algorithm for large databases. In: Proc. of ACM SIGMOD International Conference on Management of Data, pp. 73–84 (1998)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2000)
Homburg, H., Mierswa, I., Möller, B., Morik, K., Wurst, M.: A benchmark dataset for audio classification and clustering. In: Proceedings of the International Conference on Music Information Retrieval (2005)
Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: BibSonomy: A social bookmark and publication sharing system. In: Proceedings of the Conceptual Structures Tool Interoperability Workshop at the International Conference on Conceptual Structures (2006)
Kaspari, A.: Maschinelle Lernverfahren für kollaboratives tagging. Master’s thesis, Technische Univ. Dortmund, Computer Science, LS8 (2007)
Michalski, R.S., Kaufman, K.: Intelligent evolutionary design: A new approach to optimizing complex engineering systems and its application to designing heat exchangers. International Journal of Intelligent Systems 21(12) (2006)
Michalski, R.S., Stepp, R., Diday, E.: A recent advance in data analysis: Clustering objects into classes characterized by conjunctive concepts. In: Kanal, L., Rosenfeld, A. (eds.) Progress in Pattern Recognition, pp. 33–55. North-Holland, Amsterdam (1981)
Michalski, R.S., Chilausky, R.: Knowledge acquisition by encoding expert rules versus computer induction from examples: A case study involving soybean pathology. International Journal for Man-Machine Studies (12), 63–87 (1980)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: Yale: Rapid prototyping for complex data mining tasks. In: Ungar, L., Craven, M., Gunopulos, D., Eliassi-Rad, T. (eds.) KDD 2006: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 935–940. ACM, New York (2006)
Morik, K.: Balanced cooperative modeling. In: Michalski, R., Tecuci, G. (eds.) Machine Learning - A Multistrategy Approach, pp. 295–318. Morgan Kaufmann, San Francisco (1994)
Ryszard, R.S., Michalski, S.: A variable decision space approach for implementing a classification system. In: Proceedings of the Second International Joint Conference on Pattern Recognition, Copenhagen, Denmark, pp. 71–75 (1974)
Strehl, A., Ghosh, J.: Cluster ensembles – a knowledge reuse framework for combining partitionings. In: Proceedings of the AAAI (2002)
Topchy, A.P., Jain, A.K., Punch, W.F.: Combining multiple weak clusterings. In: Proceedings of the International Conference on Data Mining, pp. 331–338 (2003)
Wang, K., Xu, C., Liu, B.: Clustering transactions using large items. In: Proceedings of the International Conference on Information and Knowledge Management (1998)
Wurst, M.: Distributed Collaborative Structuring – A Data Mining Approach to Information Management in Loosely-Coupled Domains. PhD thesis, Technische Univ. Dortmund, Computer Science, LS8 (2008)
Wurst, M., Morik, K., Mierswa, I.: Localized alternative cluster ensembles for collaborative structuring. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 485–496. Springer, Heidelberg (2006)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)
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Morik, K., Wurst, M. (2010). Clustering the Web 2.0. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning II. Studies in Computational Intelligence, vol 263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05179-1_10
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DOI: https://doi.org/10.1007/978-3-642-05179-1_10
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