Outlier detection with Possibilistic Exponential Fuzzy Clustering

Treerattanapitak, Kiatichai; Jaruskulchai, Chuleerat

doi:10.1109/fskd.2011.6019597

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…The possibilistic fuzzy c-mean method was more robust and efficient for many levels of noise. To overcome the noise condition drawback of fuzzy c-mean clustering, the authors of Reference [ 47 ] proposed an exponential fuzzy c-mean to enhance membership issues that results in a more meaningful membership degree over fuzzy c-mean.…”

Section: Methodsmentioning

confidence: 99%

“…For many practical issues, clustering analyses were used to explore the data structure to understand the characteristics of data. Different clustering algorithms were proposed, including the Otsu algorithm [ 36 ], the k-means algorithm [ 54 ], the FCM algorithm [ 55 ], various improved FCM algorithms [ 43 , 44 , 45 , 46 , 47 , 48 , 49 ] and so on.…”

Section: Methodsmentioning

confidence: 99%

“…In Algorithm 1, the Intuitionist Possibilistic fuzzy c-mean methodology is written to strengthen the breast cancer detection system. Medical images are subsequently enhanced using Intuitionist Possibilistic fuzzy c-mean algorithms to form a cluster of pixels [ 47 , 48 ]. In order to improve membership assignments, a possibilistic approach has been used to overcome the noise cases.…”

Section: Proposed Intuitionistic Possibilistic Fuzzy Clusteringmentioning

confidence: 99%

“…In order to improve membership assignments, a possibilistic approach has been used to overcome the noise cases. We also proposed an integrated intuitionistic fuzzy c-mean system [ 48 , 49 ] to improve the possibilistic c-mean algorithm [ 47 ]. A medical image segmentation system referred as an intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering system.…”

Section: Proposed Intuitionistic Possibilistic Fuzzy Clusteringmentioning

confidence: 99%

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An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Chowdhary

Mittal

Kumaresan

et al. 2020

Sensors

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The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Proposed Intuitionistic Possibilistic Fuzzy Clusteringmentioning

confidence: 99%

Section: Proposed Intuitionistic Possibilistic Fuzzy Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Chowdhary

Mittal

Kumaresan

et al. 2020

Sensors

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“…Regarding this kind of problems, the possibilistic algorithms are good candidates for distinguishing outliers more efficiently. For example, Treerattanapitak & Jaruskulchai (2011) stated that integrating the possibilistic and fuzzy terms in a clustering algorithm allows detecting outliers. In their study, possibilistic exponential fuzzy clustering produced accurate results in outlier detection based on exponential outlier factor scores that are calculated from the distances to the centroids.…”

Section: Related Workmentioning

confidence: 99%

Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering

Cebecí

Cebeci

Tahtali

et al. 2022

PeerJ Computer Science

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Outliers are data points that significantly deviate from other data points in a data set because of different mechanisms or unusual processes. Outlier detection is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for data cleansing in data science. In this study, we propose two novel outlier detection approaches using the typicality degrees which are the partitioning result of unsupervised possibilistic clustering algorithms. The proposed approaches are based on finding the atypical data points below a predefined threshold value, a possibilistic level for evaluating a point as an outlier. The experiments on the synthetic and real data sets showed that the proposed approaches can be successfully used to detect outliers without considering the structure and distribution of the features in multidimensional data sets.

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Segmentation of Mammograms Using a Novel Intuitionistic Possibilistic Fuzzy C-Mean Clustering Algorithm

Chowdhary

Acharjya

2017

Advances in Intelligent Systems and Computing

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Outlier detection with Possibilistic Exponential Fuzzy Clustering

Cited by 8 publications

References 17 publications

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering

Segmentation of Mammograms Using a Novel Intuitionistic Possibilistic Fuzzy C-Mean Clustering Algorithm

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