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Abstract
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Clustering is a critical technique in data analysis, extensively applied in fields such as bioinformatics, image processing, and market segmentation. Among clustering algorithms, Fuzzy C-Means (FCM) is widely recognized for its ability to assign partial membership to data points, making it effective for overlapping cluster scenarios. However, traditional FCM faces significant challenges, including sensitivity to irrelevant features, susceptibility to noise and outliers, and inefficiencies with high-dimensional data.
This research proposes an enhanced FCM algorithm that integrates adaptive feature weighting and robust outlier handling mechanisms. The adaptive feature weighting ensures that more relevant features contribute significantly to clustering, while less important features are down-weighted. Simultaneously, the robust outlier handling approach mitigates the influence of noise and outliers, ensuring more accurate and reliable cluster formation. Additionally, the algorithm is designed to address scalability issues, optimizing performance for large and high-dimensional datasets.
The effectiveness of the proposed method will be evaluated through extensive experimentation on synthetic and real-world datasets, including the Iris and Wine datasets. The results are expected to demonstrate significant improvements in clustering accuracy, robustness, and scalability, making the algorithm a valuable tool for complex applications in medical imaging, bioinformatics, and big data analytics.
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