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Keywords:

  • clustering;
  • fuzzy conceptual clustering;
  • data mining

Abstract

Grouping unknown data into groups of similar data is a necessary first step for classification, indexing of databases, and prediction. Most of the current applications, such as news classification, blog indexing, image classification, and medical diagnosis, obtain their data in temporal sequence or online. 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 reoptimize the fuzzy-based hierarchy based on the newly arrived data are explained. Finally, we evaluate our method and present the results. The evaluation of the discovered concepts based on a decision tree classifier shows that the accuracy of the decision tree is very good for the fuzzy conceptual clustering algorithm compared with fuzzy c-means and the accuracy comes close to the expert's accuracy. Copyright © 2010 John Wiley & Sons, Ltd.