Chapter

Challenges at the Interface of Data Analysis, Computer Science, and Optimization

Part of the series Studies in Classification, Data Analysis, and Knowledge Organization pp 3-11

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Fuzzification of Agglomerative Hierarchical Crisp Clustering Algorithms

  • Mathias BankAffiliated withFaculty for Mathematics and Economics, University of Ulm Email author 
  • , Friedhelm SchwenkerAffiliated withInstitute of Neural Information Processing, University of Ulm

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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.