Fuzzy Clustering Exploiting Neighbourhood Information for Non-image Data
DOI:
https://doi.org/10.26438/ijcse/v12i2.18Keywords:
Clustering, patial FCM,, nonimage data, Euclidian neighbour, FCMSAbstract
We propose an enhanced variant of the traditional Fuzzy C-Means (FCM) algorithm tailored for leveraging neighbourhood information in non-image datasets residing in Euclidean space. Our novel methodology aims to capitalize on spatial contextual cues inherent in such datasets, thereby complementing the inherent fuzziness of individual data points. Through the incorporation of neighbourhood information, our approach extends beyond the limitations of conventional FCM, leading to improved clustering performance. We validate the efficacy of our method using synthetic and real datasets, demonstrating its superiority over conventional FCM in capturing spatial relationships within the data. Our findings underscore the effectiveness of our approach in enhancing clustering outcomes by strategically incorporating neighbourhood information into the FCM framework for non-image data in Euclidean space.
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