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.
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Perner, P., Attig, A. (2010). Fuzzy Conceptual Clustering. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_6
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DOI: https://doi.org/10.1007/978-3-642-14400-4_6
Publisher Name: Springer, Berlin, Heidelberg
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