Possibilistic C-means Clustering Algorithm Research Articles

Mahalanobis-Kernel Distance-Based Suppressed Possibilistic C-Means Clustering Algorithm for Imbalanced Image Segmentation

Mahalanobis-Kernel Distance-Based Suppressed Possibilistic C-Means Clustering Algorithm for Imbalanced Image Segmentation

Semi-supervised possibilistic c-means clustering algorithm based on feature weights for imbalanced data

The possibilistic c-means clustering (PCM) algorithm improves the robustness of fuzzy c-means clustering (FCM) to noise and outliers by releasing the probabilistic constraint of memberships. The semi-supervised possibilistic c-means clustering (SSPCM) algorithm improves the clustering effect on datasets with imbalanced sizes by introducing a small amount of label information. However, the traditional semi-supervised algorithm still faces the problem of low utilization of supervision information for datasets with large differences in sample sizes. Moreover, the Euclidean distance, which treats features equally, cannot handle feature-imbalanced data. Therefore, this paper proposes a semi-supervised possibilistic c-means clustering algorithm based on feature weights (FW-SSPCM) by introducing the ideas of supervised centers. First, the algorithm introduces the supervised center into the objective function of the SSPCM to improve the utilization rate of supervision information and thus guide the center iteration of small clusters. Second, the feature weighting strategy is introduced in the objective function to adaptively assign feature weights according to the importance of different features in different clusters, thus improving the adaptability of the algorithm to feature-imbalanced datasets. In addition, to improve the robustness of the antinoise effect and retain additional image details, a new image segmentation algorithm based on FW-SSPCM and local information (LFW-SSPCM) is proposed by introducing local spatial information obtained by bilateral filtering. Finally, through clustering experiments on synthetic data, UCI datasets and on color images characteristic of multiple features, including imbalanced sizes, imbalanced features and strong noise injection, the clustering performances of the proposed FW-SSPCM and LFW-SSPCM proposed in this paper are significantly better than those of several related clustering algorithms.

Retracted: Deep Possibilistic C-means Clustering Algorithm on Medical Datasets.

[This retracts the article DOI: 10.1155/2022/3469979.].

Deep Possibilistic C-means Clustering Algorithm on Medical Datasets.

In the past, the possibilistic C-means clustering algorithm (PCM) has proven its superiority on various medical datasets by overcoming the unstable clustering effect caused by both the hard division of traditional hard clustering models and the susceptibility of fuzzy C-means clustering algorithm (FCM) to noise. However, with the deep integration and development of the Internet of Things (IoT) as well as big data with the medical field, the width and height of medical datasets are growing bigger and bigger. In the face of high-dimensional and giant complex datasets, it is challenging for the PCM algorithm based on machine learning to extract valuable features from thousands of dimensions, which increases the computational complexity and useless time consumption and makes it difficult to avoid the quality problem of clustering. To this end, this paper proposes a deep possibilistic C-mean clustering algorithm (DPCM) that combines the traditional PCM algorithm with a special deep network called autoencoder. Taking advantage of the fact that the autoencoder can minimize the reconstruction loss and the PCM uses soft affiliation to facilitate gradient descent, DPCM allows deep neural networks and PCM's clustering centers to be optimized at the same time, so that it effectively improves the clustering efficiency and accuracy. Experiments on medical datasets with various dimensions demonstrate that this method has a better effect than traditional clustering methods, besides being able to overcome the interference of noise better.

A New Cutset-type Kernelled Possibilistic C-means Clustering Segmentation Algorithm Based on SLIC Super-pixels

: The kernelled possibilistic C-means clustering algorithm (KPCM) can effectively cluster hyper-sphere data with noise and outliers by introducing the kernelled method to the possibilistic C-means clustering (PCM) algorithm. However, the KPCM still suffers from the same coincident clustering problem as the PCM algorithm due to the lack of between-class relationships. Therefore, this paper introduces the cut-set theory into the KPCM and modifies the possibilistic memberships in the iterative process. Then a cutset-type kernelled possibilistic C-means clustering (CKPCM) algorithm is proposed to overcome the coincident clustering problem of the KPCM. Simultaneously a adaptive method of estimating the cut-set threshold is also given by averaging inter-class distances. Additionally, a cutset-type kernelled possibilistic C-means clustering segmentation algorithm based on the SLIC super-pixels (SS-C-KPCM) is also proposed to improve the segmentation quality and efficiency of the color images. Several experimental results on artificial data sets and image segmentation simulation results prove the excellent performance of the proposed algorithms in this paper.

Cutset-type Possibilistic C-means Clustering Algorithms Based on Semi-supervised Information

Cutset-type Possibilistic C-Means clustering (C-PCM) algorithm can significantly reduce the coincident clustering phenomenon of the Possibilistic C-Means clustering (PCM) algorithm by introducing the cut-set concept into the PCM. The C-PCM also has strong robustness to noise and outliers. However, the C-PCM still suffers from the center migration problem for datasets with small targets. In order to solve this problem, a Semi-Supervised Cutset-type Possibility C-Means (SS-C-PCM) clustering algorithm is proposed by introducing the semi-supervised learning mechanism into the objective function of the C-PCM and utilizing some prior information to guide the clustering process. Meanwhile, in order to improve the segmentation efficiency and accuracy of color images, a differential evolutionary superpixel-based Semi-Supervised Cutset-type Possibilistic C-Means (desSS-C-PCM) clustering algorithm is proposed. In the desSS-C-PCM, the Differential Evolutionary Superpixel(DES) algorithm is used to obtain the spatial neighborhood information of an image, which is integrated into the objective function of the semi-supervised C-PCM to improve the segmentation quality. Simultaneously, the color histogram is used to reconstruct the new objective function to reduce the computational complexity of the algorithm. Several experiments of artificial data clustering and color image segmentation show that the proposed algorithm can effectively improve the clustering effect of datasets with small targets and the execution efficiency compared with several related algorithms.

A Distributed Weighted Possibilistic c-Means Algorithm for Clustering Incomplete Big Sensor Data

Possibilistic c-means clustering algorithm (PCM) has emerged as an important technique for pattern recognition and data analysis. Owning to the existence of many missing values, PCM is difficult to produce a good clustering result in real time. The paper proposes a distributed weighted possibillistic c-means clustering algorithm (DWPCM), which works in three steps. First the paper applies the partial distance strategy to PCM (PDPCM) for calculating the distance between any two objects in the incomplete data set. Further, a weighted PDPCM algorithm (WPCM) is designed to reduce the corruption of missing values by assigning low weight values to incomplete data objects. Finally, to improve the cluster speed of WPCM, the cloud computing technology is used to optimize the WPCM algorithm by designing the distributed weighted possibilistic c-means clustering algorithm (DWPCM) based on MapReduce. The experimental results demonstrate that the proposed algorithms can produce an appropriate partition efficiently for incomplete big sensor data.

Similarity Measure based Robust Possibilistic C-means Clustering Algorithms

Similarity Measure based Robust Possibilistic C-means Clustering Algorithms