Abstract
The classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.
Highlights
As one of the most commonly used data analysis methods in machine learning, data mining, and artificial intelligence, clustering divides data sets into clusters according to their features so that the sample points in the same cluster are highly similar, while those in different clusters are dissimilar [1]
To tackle the problem of Fuzzy C-means (FCM)’s sensitivity to initial cluster centers and noise points, the improved FCM algorithm based on the initial center optimization method, density clustering, and grid clustering was proposed by Shi [15] and its effectiveness was proved by taxi trajectory data sets
These data sets are widely used in clustering tests, through which the clustering performance of LI_BIFCM in different application scenarios can be simulated
Summary
As one of the most commonly used data analysis methods in machine learning, data mining, and artificial intelligence, clustering divides data sets into clusters according to their features so that the sample points in the same cluster are highly similar, while those in different clusters are dissimilar [1]. The optimal memberships and cluster centers of sample points are obtained by iteratively calculating and are clustered following the principle of maximal membership. This work proposes a bi-directional FCM clustering ensemble technique that takes local information into account (LI_BIFCM) to address the drawbacks of FCM. The. International Journal of Computational Intelligence Systems (2021) 14:171 suggested technique considers not just clusters diversity and sample points local information. 3. LI_BIFCM increases clustering performance even further by employing horizontal and vertical ensembles.
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