Fuzzy conceptual clustering

Authors:
Petra Perner;Anja Attig
Affiliations:
Institute of Computer Vision and applied Computer Sciences, IBaI, Leipzig, Germany;Institute of Computer Vision and applied Computer Sciences, IBaI, Leipzig, Germany
Venue:
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Year:
2010

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Abstract

Grouping unknown data into groups of similar data is a necessary first step for classification, indexing of data bases, and prediction. Most of today's applications, such as news classification, blog indexing, image classification, and medical diagnosis, obtain their data in temporal sequence or on-line. The necessity for data exploration requires a graphical method that allows the expert in the field to study the determined groups of data. Therefore, incremental hierarchical clustering methods that can create explicit cluster descriptions are convenient. The noisy and uncertain nature of the data makes it necessary to develop fuzzy clustering methods. We propose a novel fuzzy conceptual clustering algorithm. We describe the fuzzy objective function for incremental building of the clusters and the relation among the clusters in a hierarchy. The operations that can incrementally re-optimize the fuzzy-based hierarchy based on the newly arrived data are explained. Finally, we evaluate our method and present results.