%0 Conference Proceedings %T A Visual Quality Index for Fuzzy C-Means %+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC) %+ Archéologie et Archéométrie (ArAr) %A Oztürk, Aybükë %A Lallich, Stéphane %A Darmont, Jérôme %Z Région Auvergne-Rhône-Alpes %Z Part 12: Fuzzy %< avec comité de lecture %( IFIP Advances in Information and Communication Technology %B 14th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2018) %C Rhodes, Greece %Y Lazaros Iliadis %Y Ilias Maglogiannis %Y Vassilis Plagianakos %I Springer %3 Artificial Intelligence Applications and Innovations %V AICT-519 %P 546-555 %8 2018-05-25 %D 2018 %Z 1806.01552 %R 10.1007/978-3-319-92007-8_46 %K Fuzzy Clustering %K Fuzzy C-Means %K Quality Indices %K Visual Index %K Elbow Rule %Z Computer Science [cs]/Machine Learning [cs.LG]Conference papers %X Cluster analysis is widely used in the areas of machine learning and data mining. Fuzzy clustering is a particular method that considers that a data point can belong to more than one cluster. Fuzzy clustering helps obtain flexible clusters, as needed in such applications as text categorization. The performance of a clustering algorithm critically depends on the number of clusters, and estimating the optimal number of clusters is a challenging task. Quality indices help estimate the optimal number of clusters. However, there is no quality index that can obtain an accurate number of clusters for different datasets. Thence, in this paper, we propose a new cluster quality index associated with a visual, graph-based solution that helps choose the optimal number of clusters in fuzzy partitions. Moreover, we validate our theoretical results through extensive comparison experiments against state-of-the-art quality indices on a variety of numerical real-world and artificial datasets. %G English %Z TC 12 %Z WG 12.5 %2 https://hal.science/hal-01727426/document %2 https://hal.science/hal-01727426/file/A%20Visual%20Quality%20Index%20for%20Fuzzy%20C-Means.pdf %L hal-01727426 %U https://hal.science/hal-01727426 %~ CNRS %~ UNIV-LYON1 %~ UNIV-LYON2 %~ ERIC %~ IFIP %~ IFIP-AICT %~ IFIP-TC %~ MOM %~ IFIP-WG %~ IFIP-TC12 %~ IFIP-AIAI %~ IFIP-WG12-5 %~ LABEXIMU %~ IFIP-AICT-519 %~ LYON2 %~ UDL %~ UNIV-LYON %~ TEST3-HALCNRS