Abstract
Clustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering considers that each data point belongs to one and only one cluster. However, it is inadequate as some data points may belong to several clusters, as is the case in text categorization. Thus, we need more flexible clustering. Fuzzy clustering methods, where each data point can belong to several clusters, are an interesting alternative. Yet, seeding iterative fuzzy algorithms to achieve high quality clustering is an issue. In this paper, we propose a new linear and efficient initialization algorithm MaxMin Linear to deal with this problem. Then, we validate our theoretical results through extensive experiments on a variety of numerical real-world and artificial datasets. We also test several validity indices, including a new validity index that we propose, Transformed Standardized Fuzzy Difference (TSFD).
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Acknowledgements
This project is supported by the Rhône Alpes Region’s ARC 5: “Cultures, Sciences, Sociétés et Médiations” through A. Öztürk’s Ph.D. grant.
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Öztürk, A., Lallich, S., Darmont, J., Waksman, S.Y. (2018). MaxMin Linear Initialization for Fuzzy C-Means. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_1
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DOI: https://doi.org/10.1007/978-3-319-96136-1_1
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