Feature weighted multi-view possibilistic c-means with feature reduction

Datasets that are gathered from multiple sources are called multi-view datasets. These types of datasets represent different sets of feature attributes to form different views. Advanced computing and information technology have made it possible to collect and store a massive amount of data. As more features are added to each view, the data becomes sparse, and analysis suffers from the curse of dimensionality. Exploring and integrating diverse information from different views have been the focus of many approaches to improve clustering performance. The relationships among distinct views should not only be explored and analyze but should also consider the emerging high-dimensionality of each view to further improve the clustering performance. Recently, Yang and Benjamin proposed a clustering algorithm called "Feature Weighted Reduction PCM (FW-R-PCM) that calculates feature weights to identify relevant features and consequently, eliminates features that are irrelevant to reduce feature dimension from the entire feature space. In this paper, we propose a feature-weighted multi-view possibilistic c-means (FW-R-MVPCM) clustering algorithm, which extends FW-R-PCM to consider clustering high-dimensional multi-view datasets and perform feature reduction simultaneously. Our proposed FW-R-MVPCM can effectively improve the clustering performance using a weighting scheme that identifies, and selects relevant features from each view, and eliminate the irrelevant features, thus reducing the dimension of features in each view. Experiments on real datasets are performed to analyze the theoretical behavior of FW-R-MVPCM and to show its usefulness and effectiveness. Furthermore, FW-R-MVPCM is compared with FW-R-PCM, and other multi-view clustering algorithms such as WMCFS, SWVF, W-MV-PCM-L2, and WV-Co-FCM.