TY - JOUR ID - 133277 TI - A new approach to the fuzzy c-means clustering algorithm by automatic weights and local clustering JO - Passer Journal of Basic and Applied Sciences JA - PSR LA - en SN - 27065944 AU - Abdulrahman, Yadgar Sirwan AD - University of Garmian, Kalar, Iraq. Y1 - 2021 PY - 2021 VL - 3 IS - 1 SP - 95 EP - 101 KW - Keywords: Clustering KW - Fuzzy Clustering KW - the sum of square errors KW - Local feature weighting KW - Non-Euclidean metric KW - Fuzzy c-means clustering DO - 10.24271/psr.18 N2 - Clustering is one of the essential strategies in data analysis. In classical solutions, all features are assumed to contribute equally to the data clustering. Of course, some features are more important than others in real data sets. As a result, essential features will have a more significant impact on identifying optimal clusters than other features. In this article, a fuzzy clustering algorithm with local automatic weighting is presented. The proposed algorithm has many advantages such as: 1) the weights perform features locally, meaning that each cluster's weight is different from the rest. 2) calculating the distance between the samples using a non-euclidian similarity criterion to reduce the noise effect. 3) the weight of the features is obtained comparatively during the learning process. In this study, mathematical analyzes were done to obtain the clustering centers well-being and the features' weights. Experiments were done on the data set range to represent the progressive algorithm's efficiency compared to other proposed algorithms with global and local features. UR - https://passer.garmian.edu.krd/article_133277.html L1 - https://passer.garmian.edu.krd/article_133277_7f61ccf63fe2f7a2d5b0b2c52401631d.pdf ER -