BPEC: Belief-Peaks Evidential Clustering

International audienceThis paper introduces a new evidential clustering method based on the notion of "belief peaks" in the framework of belief functions. The basic idea is that all data objects in the neighborhood of each sample provide pieces of evidence that induce belief on the possibility of such sample to become a cluster center. A sample having higher belief than its neighbors and located far away from other local maxima is then characterized as cluster center. Finally, a credal partition is created by minimizing an objective function with the fixed cluster centers. An adaptive distance metric is used to fit for unknown shapes of data structures. We show that the proposed evidential clustering procedure has very good performance with an ability to reveal the data structure in the form of a credal partition, from which hard, fuzzy, possibilistic and rough partitions can be derived. Simulations on synthetic and real-world datasets validate our conclusions

Adaptive scaling of cluster boundaries for large-scale social media data clustering

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters

Density propagation based adaptive multi-density clustering algorithm

This research was supported by the Science & Technology Development Foundation of Jilin Province (Grants Nos. 20160101259JC, 20180201045GX), the National Natural Science Foundation of China (Grants No. 61772227) and the Natural Science Foundation of Xinjiang Province (Grants No. 2015211C127). This resarch is also supported by the Engineering and Physical Sciences Research Council (EPSRC) funded project on New Industrial Systems: Manufacturing Immortality (EP/R020957/1).Peer reviewedPublisher PD

A Wolf Pack Optimization Theory Based Improved Density Peaks Clustering Approach

In view of the problem that the Density Peaks Clustering (DPC) algorithm needs to manually set the parameter cut-off distance (dc) we propose a Wolf Pack optimization theory based Density Peaks Clustering approach (WPA-DPC). Firstly, we introduce dc parameter into the Wolf Pack Algorithm (WPA) to speed up the search. Secondly, we introduce the WPA into the DPC algorithm; the cut-off distance is used as the location of the wolf group. Finally, we make silhouette index in the search process as the fitness value, and the optimal location of the wolf group is the parameter value at the end. The simulation results show that compared with the traditional Density Peaks Clustering algorithm, the proposed algorithm is closer to the true clustering number. According to the evaluation results of silhouette and f-measure, the quality of clustering and the accuracy are greatly improved