Gaussian Collaborative Fuzzy C-Means Clustering

Gao, Yunlong; Wang, Zhihao; Li, Huidui; Pan, Jinyan

doi:10.1007/s40815-021-01090-1

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…The simplicity of the FCM algorithm makes it very robust in most cases, and it has strong vitality in clustering algorithms. [24] Because FCM is supported by fuzzy theory, it is mainly described by fuzzy partition matrix, which breaks the limitation that each data point in hard clustering can only be divided into one category. The final output fuzzy matrix reflects all the information of the data set, so the distribution and overall characteristics of the data set can be accurately examined [25].…”

Section: Fuzzy C-means (Fcm) Methodsmentioning

confidence: 99%

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Xie¹,

Qiao²,

Xia³

et al. 2023

Preprint

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In recent years, the problem of fuzzy clustering has been widely concerned. The membership iteration of existing methods is mostly considered globally, which has considerable problems in noisy environments, and iterative calculations for clusters with a large number of different sample sizes are not accurate and efficient. In this paper, starting from the strategy of large-scale priority, the data is fuzzy iterated using granularballs, and the membership degree of data only considers the two granular-balls where it is located, thus improving the efficiency of iteration. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios, which enhances the practicability of fuzzy clustering calculations.

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Section: Fuzzy C-means (Fcm) Methodsmentioning

confidence: 99%

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Xie¹,

Qiao²,

Xia³

et al. 2023

Preprint

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“…The majority of the aforementioned studies, which produced good results in heart disease prediction, employed supervised and deep learning algorithms, both of which need extensive labelled data [16][17][18][19][20][21][22]. Several researchers have used collaborative clustering, which significantly improves the clustering outcomes [23][24][25][26][27].…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Heart Disease Using Collaborative Clustering and Ensemble Learning Techniques

Al-Sayed,

Khayyat,

Zamzami

2023

Applied Sciences

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Different data types are frequently included in clinical data. Applying machine learning algorithms to mixed data can be difficult and impact the output accuracy and quality. This paper proposes a hybrid model of unsupervised and supervised learning techniques, which can be used in modelling and processing mixed data with an application in heart disease diagnosis. The model consists of two main components: collaborative clustering and combining decisions (the ensemble approach). The mixed data clustering problem is considered as a multi-view clustering problem; each view is processed using specialised clustering algorithms. Since each algorithm operates on a different space of the data set’s features, a novel collaborative framework was proposed that promotes the clustering process through information exchange between the different clustering algorithms, thereby producing expert models that model other spaces of the data set’s features. The expectation maximisation algorithm forms the foundation for this optimisation process, enhancing the collaborative term representing entropy; excellent convergence characteristics are therefore ensured. An ensemble approach similar to the stacking approach was used. The logistic regression model was utilised as a meta-classifier, training the expert model prediction results, and was subsequently used to predict the final output. The results prove the efficacy of this collaborative approach in optimising different clustering algorithms and meta-classifier outcomes.

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“…EFCM provides a sparser description for reliable points and a fuzzier description for marginal points of clusters, thus, the roles of reliable and margin points are more balance. In [27], Gaussian mixture model and collaborative technology are combined with FCM to enhance the ability of recognising the distribution of intra-cluster. This approach is effective in dealing with noise, non-spherical clusters, size-imbalanced clusters.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Rough Intrigued Harmonic Discrepancy Clustering

Yue

Yang

et al. 2023

IEEE Trans. Fuzzy Syst.

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Fuzzy clustering decomposes data into clusters using partial memberships by exploring the cluster structure information, which demonstrates comparable performance for knowledge exploitation under the circumstance of information incompleteness. In general, this scheme considers the memberships of objects to cluster centroids and applies to clusters with the spherical distribution. In addition, the noises and outliers may significantly influence the clustering process; a common mitigation measure is the application of separate noise processing algorithms, but this usually introduces multiple parameters which are challenging to be determined for different data types. This paper proposes a new fuzzy-rough intrigued harmonic discrepancy clustering (HDC) algorithm by noting that fuzzy-rough sets offer a higher degree of uncertainty modelling for both vagueness and imprecision present in real-valued datasets. The HDC is implemented by introducing a novel concept of harmonic discrepancy, which effectively indicates the dissimilarity between a data instance and foreign clusters with their distributions fully considered. The proposed HDC is thus featured by a powerful processing ability on complex data distribution leading to enhanced clustering performance, particularly on noisy datasets, without the use of explicit noise handling parameters. The experimental results confirm the effectiveness of the proposed HDC, which generally outperforms the popular representative clustering algorithms on both synthetic and benchmark datasets, demonstrating the superiority of the proposed algorithm.

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Gaussian Collaborative Fuzzy C-Means Clustering

Cited by 12 publications

References 43 publications

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Predicting Heart Disease Using Collaborative Clustering and Ensemble Learning Techniques

Fuzzy-Rough Intrigued Harmonic Discrepancy Clustering

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