A New Feature-Weighted Relative Entropy Clustering Algorithm

The fuzzy C-means (FCM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition learning. However, real-world data is more complex and there may be some irrelevant features in the data that affect the final clustering results of FCM. The weighted clustering algorithm increases the importance of relevant features in the data by assigning different weights to features of different dimensions, and at the same time weakens the influence of irrelevant features on the clustering results. However, both the weighted clustering algorithm and the FCM algorithm will have classification errors as the observation noise increases. As a distance measure between two distributions, relative entropy is added to the objective function as a regularization function, which can minimize the distance within the cluster and maximize the difference between clusters. Therefore, this paper proposes a new feature-weighted relative entropy clustering algorithm (REFCM_EW). The REFCM_EW algorithm combines feature weight and relative entropy, which not only enhances the importance of relevant features in the data but also has better noise detection capability. Experimental results show that REFCM_EW has a good effect on the strip data.