A Novel Clustering Validity Function of FCM Clustering Algorithm

Zhu, Lei; Wang, Jiesheng; Wang, H. Y.

doi:10.1109/access.2019.2946599

Cited by 32 publications

(15 citation statements)

References 28 publications

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“…Dynamic fuzzy c-means clustering algorithm [33] is a dataset partitioning method, which obtains the membership degree of each sample point to all clustering centers through objective function optimization, and then determines the category attributes of sample points. Different clustering numbers are set, and the coupling degree and separation degree between signal samples are calculated respectively [34]. The clustering results corresponding to different clustering numbers are evaluated, and the optimal clustering result is selected.…”

Section: B the Dynamic Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

A Fuzzy Radial Basis Inference Network With Grouped Signal Feature Embedding and Its Application in Multi-Source Signals Classification

Zha

Liu

et al. 2021

IEEE Access

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A fuzzy radial basis inference network with grouped signal feature embedding (GFE-FINN) classification model is proposed for multi-source time-varying signal fusion analysis and feature knowledge embedding, which multi-channel signals are divided into several groups according to the sources, attribute, features and sensitivity of signals. Each pattern class of the grouped signal sample set is divided into several pattern subclasses which are more similar features according to the grouping index, and typical feature samples are extracted to implicitly express the category features knowledge of the grouped signal. A fuzzy radial basis process neuron (FRBPN) is defined, which is used as parametric membership functions, and the typical feature signal samples of the grouped pattern subclass are used as the kernel centers of FRBPN to realize the embedding of the diverse feature knowledge. Through the kernel transformation in FRBPN, the input signals of each group are fuzzified respectively. Fuzzy multiplication operation is used to realize the information synthesis based on the membership degree of grouped pattern subclasses and establish fuzzy reasoning and classification rules. The proposed method can realize the feature fusion based on fuzzy membership degree and the semantic representation based on fuzzy rules hierarchically. Through the learning of the sample set, fuzzy membership function, reasoning and classification rules are established adaptively. A comprehensive learning algorithm was given. An experiment was conducted using 4-groups 12-lead long ECG signals in diagnosis of difficult heart disease. The correct recognition rate reaches 87.95%, and the performance evaluation index and generalization ability are significantly improved. INDEX TERMSMulti-source time-varying signal classification, signal grouping, feature knowledge embedding, parametric membership function, fuzzy reasoning rules

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Section: B the Dynamic Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

A Fuzzy Radial Basis Inference Network With Grouped Signal Feature Embedding and Its Application in Multi-Source Signals Classification

Zha

Liu

et al. 2021

IEEE Access

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“…Non [1]. The aim of clustering is to separate data into partitions with elements that have similar characteristics between them.…”

Section: Nicmentioning

confidence: 99%

An Automatic Merge Technique to Improve the Clustering Quality Performed by LAMDA

Morales

Aguilar

2020

IEEE Access

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Clustering is a research challenge focused on discovering knowledge from data samples whose goal is to build good quality partitions. In this paper is proposed an approach based on LAMDA (Learning Algorithm for Multivariable Data Analysis), whose most important features are: a) it is a non-iterative fuzzy algorithm that can work with online data streams, b) it does not require the number of clusters, c) it can generate new partitions with objects that do not have enough similarity with the preexisting clusters (incremental-learning). However, in some applications, the number of created partitions does not correspond with the number of desired clusters, which can be excessive or impractical for the expert. Therefore, our contribution is the formalization of an automatic merge technique to update the cluster partition performed by LAMDA to improve the quality of the clusters, and a new methodology to compute the Marginal Adequacy Degree that enhances the individual-cluster assignment. The proposal, called LAMDA-RD, is applied to several benchmarks, comparing the results against the original LAMDA and other clustering algorithms, to evaluate the performance based on different metrics. Finally, LAMDA-RD is validated in a real case study related to the identification of production states in a gas-lift well, with data stream. The results have shown that LAMDA-RD achieves a competitive performance with respect to the other well-known algorithms, especially in unbalanced benchmarks and benchmarks with an overlapping of around 9%. In these cases, our algorithm is the best, reaching a Rand Index (RI) > 98%. Besides, it is consistently among the best for all metrics considered (Silhouette coefficient, modification of the Silhouette coefficient, WB-index, Performance Coefficient, among others) in all case studies analyzed in this paper. Finally, in the real case study, it is better in all the metrics.

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“…Clustering is an important unsupervised method, and the purpose of clustering is to divide a dataset into multiple clusters (or classes) with high intra-cluster similarity and low inter-cluster similarity. There have been many clustering algorithms, such as k-means (KM) and its variants [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Others are based on minimal spanning trees [ 17 , 18 , 19 ], density analysis [ 20 , 21 , 22 , 23 , 24 , 25 ], spectral analysis [ 26 , 27 ], subspace clustering [ 28 , 29 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Kernel Probabilistic K-Means Clustering

Liu

Zhang

et al. 2021

Sensors

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Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with constraints of linear equalities and linear inequalities. To accelerate the AGP method, a fast AGP (FAGP) algorithm was designed. The proposed FAGP uses a maximum-step strategy to estimate the step length, and uses an iterative method to update the projection matrix. Experiments demonstrated the effectiveness of the proposed method through a performance comparison of KPKM with KFCM, KKM, FCM and k-means. Experiments showed that the proposed KPKM is able to find nonlinearly separable structures in synthetic datasets. Ten real UCI datasets were used in this study, and KPKM had better clustering performance on at least six datsets. The proposed fast AGP requires less running time than the original AGP, and it reduced running time by 76–95% on real datasets.

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A Novel Clustering Validity Function of FCM Clustering Algorithm

Cited by 32 publications

References 28 publications

A Fuzzy Radial Basis Inference Network With Grouped Signal Feature Embedding and Its Application in Multi-Source Signals Classification

A Fuzzy Radial Basis Inference Network With Grouped Signal Feature Embedding and Its Application in Multi-Source Signals Classification

An Automatic Merge Technique to Improve the Clustering Quality Performed by LAMDA

Kernel Probabilistic K-Means Clustering

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