A possibilistic fuzzy c-means clustering algorithm

Pal, Nikhil R.; Pal, Krishan; Keller, James M.; Bezdek, James C.

doi:10.1109/tfuzz.2004.840099

Cited by 1,113 publications

(503 citation statements)

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“…To accomplish this, the partitioning constraint in the FCM algorithm is relaxed, yielding a 'possibilistic' type of membership, which, together with the penalty term imposed on the PCM algorithm, guarantees the realisation of relative values for degrees of membership as needed to enhance parameter estimates [26,30]. Additionally, the PFCM clustering algorithm is useful in overcoming sensitivity to noise and in avoiding coincident clusters, while still being able to produce the usual point prototypes or cluster centres for each cluster [26,30].…”

Section: Possibilistic Fuzzy C-means Clustering Methodsmentioning

confidence: 99%

“…In the next section, we highlight a few properties of the Possibilistic Fuzzy C-Means (PFCM) [26] clustering algorithm and discuss its integration as base cluster generating algorithm of our proposed EAC ensemble technique, which, for uniformity, we shall refer to as the pEAC ensemble technique. Moreover, since it has been shown that the performance of both the hEAC and fEAC ensemble techniques varies with type of data [27] our new ensemble clustering technique should offer a wider range of utility.…”

Section: Drawbacks Of Heac and Feac Ensemble Clustering Techniquesmentioning

confidence: 99%

“…The Possibilistic FCM (PFCM) clustering approach, which is a hybrid unsupervised method that combines the FCM algorithm with the Possibilistic C-Means (PCM) algorithm, is obtained by relaxing the partitioning constraint of the FCM algorithm so that a 'possibilistic' type of membership is realised [26,27]. Furthermore, by integrating a penalty term into the PCM, the relative values of degrees of membership that are needed for enhanced parameter estimation [26] are guaranteed.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, by integrating a penalty term into the PCM, the relative values of degrees of membership that are needed for enhanced parameter estimation [26] are guaranteed. The resulting PFCM clustering algorithm combines the intuitionistic degrees of membership of FCM with the possibilistic typicality of PCM in order to avoid noise sensitivity and to overcome coincident clusters, while maintaining the usual cluster centres for each cluster.…”

Section: Introductionmentioning

confidence: 99%

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Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis

Iliyasu

Fatichah

2016

Automatika

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Original scientific paperTraditionally, supervised machine learning methods are the first choice for tasks involving classification of data. This study provides a non-conventional hybrid alternative technique (pEAC) that blends the Possibilistic Fuzzy CMeans (PFCM) as base cluster generating algorithm into the 'standard' Evidence Accumulation Clustering (EAC) clustering method. The PFCM coalesces the separate properties of the Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) algorithms into a sophisticated clustering algorithm. Notwithstanding the tremendous capabilities offered by this hybrid technique, in terms of structure, it resembles the hEAC and fEAC ensemble clustering techniques that are realised by integrating the K-Means and FCM clustering algorithms into the EAC technique. To validate the new technique's effectiveness, its performance on both synthetic and real medical datasets was evaluated alongside individual runs of well-known clustering methods, other unsupervised ensemble clustering techniques and some supervised machine learning methods. Our results show that the proposed pEAC technique outperformed the individual runs of the clustering methods and other unsupervised ensemble techniques in terms accuracy for the diagnosis of hepatitis, cardiovascular, breast cancer, and diabetes ailments that were used in the experiments. Remarkably, compared alongside selected supervised machine learning classification models, our proposed pEAC ensemble technique exhibits better diagnosing accuracy for the two breast cancer datasets that were used, which suggests that even at the cost of none labelling of data, the proposed technique offers efficient medical data classification.Key words: Evidence accumulation clustering, K-means, fuzzy C-means, possibilitic fuzzy C-means, hybrid intelligent systems, health informatics, medical data classification, disease diagnosis.Grupiranje zasnovano na skupljanju dokaza s vjerojatnosno-neizrazitim C-means pristupom za dijagnozu bolesti. Tradicionalno, metode nadziranog strojnog učenja predstavljaju prvi izbor za zadatke koji uključuju klasifikaciju podataka. Ovo istraživanje prikazuje nekonvencionalnu hibridnu alternativnu (pEAC) tehniku koja kombinira vjerojatnosno-neizraziti C-Means (PFCM) kao osnovni algoritam grupiranja u standardno grupiranje korištenjem grupiranja zasnovanog na skupljanju dokaza (EAC). PFCM objedinjuje zasebna svojstva vjerojatnosnog C-Means (PCM) i neizrazitog C-Means (FCM) algoritama u sofisticirani algoritam grupiranja. Usprkos ogromnim mogućnostima koje nudi ova tehnika, u smislu strukture, ona je nalik cjelovitim hEAC i fEAC tehnikama grupiranja realiziranim integracijom K-Means i FCM algoritama grupiranja u EAC tehniku. Kako bi se validirala učinkovi-tost, njeno ponašanje je ispitano na sintetičkim i stvarnim medicinskim podacima te su provedene usporedbe s pojedinačnim široko rasprostranjenim metodama, drugim nenadziranim tehnikama grupiranja i nekim nadziranim metodama učenja. Rezultat prikazuje kako predložena pEAC tehnika nadmašuje poje...

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Section: Possibilistic Fuzzy C-means Clustering Methodsmentioning

confidence: 99%

Section: Drawbacks Of Heac and Feac Ensemble Clustering Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis

Iliyasu

Fatichah

2016

Automatika

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“…To deal with this problem, it is possible to use clustering techniques in order to reduce the number of fuzzy rules. One of the most commonly used clustering approaches is fuzzy c-means (FCM) [24]. This technique was originally proposed by Dunn [25] and later extended by Bezdek [26].…”

Section: B Neuro-fuzzy Structurementioning

confidence: 99%

Application of Fuzzy Modeling and Optimization in Enzymatic Esterification Process

Chaibakhsh¹,

Chaibakhsh²,

Rahman³

2011

IJCEA

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Abstract-In this paper, an application of a neuro-fuzzy modeling approach is presented in order to characterize the essential behavior of enzymatic esterification processes. The accuracy of the developed model was validated by comparing the response of the model and actual experimental data. The simulation results showed good generalization of the proposed model and its ability to predict the reaction yield, where the error of prediction for training data was less than 3%, and for validating and testing data less than 3 and 1.5%, respectively. A model-based optimization was performed to obtain the best operating conditions by using genetic algorithm. A fair comparison between the optimization results obtained from simulation experiments and laboratory data indicated the accuracy and feasibility of the proposed approach for estimating the optimal profiles in biotechnological processes. This can further facilitate up-scaling of the process by selecting the appropriate combinations of potential manufacturing parameters.Index Terms-Enzymatic esterification, experimental data, model-based optimization, fuzzy logic system, clustering approach, genetic algorithm.

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2008

Clustering

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A possibilistic fuzzy c-means clustering algorithm

Cited by 1,113 publications

References 10 publications

Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis

Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis

Application of Fuzzy Modeling and Optimization in Enzymatic Esterification Process

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