Fuzzification of Agglomerative Hierarchical Crisp Clustering Algorithms

Conference paper

DOI: 10.1007/978-3-642-24466-7_1

Part of the book series Studies in Classification, Data Analysis, and Knowledge Organization (STUDIES CLASS)
Cite this paper as:
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


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.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Faculty for Mathematics and EconomicsUniversity of UlmUlmGermany
  2. 2.Institute of Neural Information ProcessingUniversity of UlmUlmGermany