A robust fuzzy clustering model for fuzzy data based on an adaptive weighted L۱ norm
Publish place: Iranian Journal of Fuzzy Systems، Vol: 20، Issue: 6
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
View: 87
This Paper With 20 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJFS-20-6_001
تاریخ نمایه سازی: 18 آذر 1402
Abstract:
The imprecision related to measurements can be managed in terms of fuzzy features, which are characterized by two components: center and spread. Outliers affect the outcome of the clustering models. In trying to overcome this problem, this paper proposes a fuzzy clustering model for L-R fuzzy data, which is based on a dissimilarity measure between each pair of fuzzy data defined as an adaptive weighted sum of the L۱-norms of the centers and the spreads. The proposed method is robust based on the metric and weighting approaches. It estimates the weight of a given fuzzy feature on a given fuzzy cluster by considering the relevance of that feature to the cluster; if outlier fuzzy features are present in the dataset, it tends to assign them weights close to ۰.To deeply investigate the capability of our model, i.e., alleviating undesirable effects of outlier fuzzy data, we provide a wide simulation study. We consider the ability to classify correctly and the ability to recover the true prototypes, both in the presence of outliers. The comparison made with other existing robust methods indicates that the proposed methodology is more robust to the presence of outliers than other methods. Moreover, the performance of our method decreases more slowly than others when the percentage of outliers increases. An application of the suggested method to a real-world categorical dataset is also provided.
Keywords:
Authors
Elham Eskandari
Department of Mathematics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
Alireza Khastan
Department of Mathematics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :