Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources

Abstract

Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be represented by different sets of features to form different views of it. Many approaches have been proposed to improve clustering performance by exploring and integrating heterogeneous information underlying different views. In this paper, we propose a new multi-view fuzzy clustering approach called Mini-maxFCM by using minimax optimization based on well-known Fuzzy c means.In MinimaxFCM the consensus clustering results are generated based on minimax optimization in which the maximum disagreements of different weighted views are minimized. Moreover, the weight of each view can be learned automatically in the clustering process. In addition, there is only one parameter to be set besides the fuzzifier. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed MinimaxFCM are provided here. Experimental studies on nine multi-view data sets including real

Multiview cluster aggregation and splitting, with an application to multiomic breast cancer data

“…The aggregation and splitting procedures presented hereafter are respectively based on the minimization of a criterion that is inspired by the one used in the multi-view fuzzy K-means algorithm (Wang and Chen, 2017):…”

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

“…The expectation‐maximization (EM) algorithm is a statistical analysis of the probability approach, which performs the clustering operation based on cluster center initialization . The fuzzy K‐means (FKM) algorithm is mostly employed for medical image segmentation . Aparajeeta et al suggested a possibilistic based fuzzy approach that supports the eradication of noise levels in the segmentation results.…”

“…18 The fuzzy K-means (FKM) algorithm is mostly employed for medical image segmentation. 19 Aparajeeta et al 20 suggested a possibilistic based fuzzy approach that supports the eradication of noise levels in the segmentation results. The performance of the process using the spatial information of the nearest pixels is to enhance the brain image segmentation.…”

“…refers to the number of pixels in the enclosed set. The calculation of the Jaccard Index is defined in Equation (19).…”

“…The medical image segmentation algorithms referred in the Tables 2 to 4 offer the Jaccard value, which is derived using Equation 19. Table 5 illustrates the Jaccard Index values for input MR images with and without noise signals, and the SOM-based MFKM algorithm offers the highest Jaccard value, which is better than the results proffered by FCM_S1 and SOM-based FKM algorithms.…”