Abstract
User generated content from fora, weblogs and other social networks is a very fast growing data source in which different information extraction algorithms can provide a convenient data access. Hierarchical clustering algorithms are used to provide topics covered in this data on different levels of abstraction. During the last years, there has been some research using hierarchical fuzzy algorithms to handle comments not dealing with one topic but many different topics at once. The used variants of the well-known fuzzy c-means algorithm are nondeterministic and thus the cluster results are irreproducible. In this work, we present a deterministic algorithm that fuzzifies currently available agglomerative hierarchical crisp clustering algorithms and therefore allows arbitrary multi-assignments. It is shown how to reuse well-studied linkage metrics while the monotonic behavior is analyzed for each of them. The proposed algorithm is evaluated using collections of the RCV1 and RCV2 corpus.
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References
Bäck C, Hussain M (1996) Validity measures for fuzzy partitions. In: Bock HH, Polasek W (eds) Data analysis and information systems. Springer, Berlin, pp 114–125
Batagelj V (1981) Note on ultrametric hierarchical clustering algorithms. Psychometrika 46:351–352
Bordogna G, Pasi G (2009) Hierarchical-hyperspherical divisive fuzzy c-means (h2d-fcm) clustering for information retrieval. In: WI-IAT ’09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, IEEE Computer Society, pp 614–621
Diday E (1987) Orders and overlapping clusters by pyramids. Tech. Rep. RR-0730, INRIA, URL http://hal.inria.fr/inria-00075822/en/
Johnson SC (1967) Hierarchical clustering schemes. Psychometrika 32:241–254
Lance GN, Williams WT (1966) A generalized sorting strategy for computer classifications. Nature 212:218–219, DOI10.1038/212218a0
Lance GN, Williams WT (1967) A general theory of classificatory sorting strategies 1. hierarchical systems. Comput J 9(4):373–380
Mendes MES, Sacks L (2003) Evaluating fuzzy clustering for relevance-based information access. In: IEEE International Conference on Fuzzy Systems, pp 648–653
Mendes-Rodrigues MES, Sacks L (2005) A scalable hierarchical fuzzy clustering algorithm for text mining. In: The 5th International Conference on Recent Advances in Soft Computing, URL http://lesacks.googlepages.com/rasc2004.pdf
Milligan GW (1979) Ultrametric hierarchical clustering algorithms. Psychometrika 44:343–346
Nuovo AGD, Catania V (2007) On external measures for validation of fuzzy partitions. In: Foundations of Fuzzy Logic and Soft Computing. Springer, Berlin, pp 491–501
Torra V (2005) Fuzzy c-means for fuzzy hierachical clustering. In: FUZZ ’05: The 14th IEEE International Conference on Fuzzy Systems, pp 646–651
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Bank, M., Schwenker, F. (2012). Fuzzification of Agglomerative Hierarchical Crisp Clustering Algorithms. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_1
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DOI: https://doi.org/10.1007/978-3-642-24466-7_1
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