ةصلاخلا بكرملل ةدیدج تاقتشم ریضحت ىلا ثحبلا يمری 4,3,1 ةیتلاا تلاعافتلا ءارجا للاخ نم لوزایادایاث : ًلاوأ : ب كرملل فیش ةدعاق ریضحت ) 2 و نیما 5 و تبكرم 4,3,1 لوزا یادایاث ( يلیفو یلكوینلا ضیو عتلا ءارجاو ةدعاقلل يرتسلاا ب كرملا نیو كت ى لا يدؤ یل مویدو صلا دیسكوثیا دو جوب ل ثیلاا تاتیسا ومورب عم ) 3 ( ، يذ لا یازاردیھلا عم ھتلعافم تمت ن 99 % د یازاردیھلا قتشم ریضحتل ) 4 ( فیش دعاوق نم د یدع...

In this paper an optimized fuzzy logic based segmentation for abnormal MRI brain images analysis is presented. A conventional fuzzy c-means (FCM) technique does not use the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The FCM algorithm that incorporates spatial information into the m...

[1] In this paper, classification of a large hydrochemical data set (more than 600 water samples and 11 hydrochemical variables) from southeastern California by fuzzy c-means (FCM) and hierarchical cluster analysis (HCA) clustering techniques is performed and its application to hydrochemical facies delineation is discussed. Results from both FCM and HCA clustering produced cluster centers (prot...

In this paper, an improved fuzzy c-means (IFCM) clustering algorithm for image segmentation is presented. The originality of this algorithm is based on the fact that the conventional FCM-based algorithm considers no spatial context information, which makes it sensitive to noise. The new algorithm is formulated by incorporating the spatial neighborhood information into the original FCM algorithm...

Fuzzy c-means (FCM) is an important clustering algorithm. However, it does not consider the impact of different feature on clustering. In this paper, we present a fuzzy clustering algorithm with the generalized entropy of feature weights FCM (GEWFCM). By introducing feature weights and adding regularized term of their generalized entropy, a new objective function is proposed in terms of objecti...

Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomizationbased method to control the false positive rate and estimate statistical significance of the FCM results. Using this novel appr...

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM-Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (...

Many functionals have been proposed for validation of partitions of object data produced by the fuzzy c-means (FCM) clustering algorithm. We examine the role a subtle but important parameter-the weighting exponent m of the FCM model-plays in determining the validity of FCM partitions. The functionals considered are the partition coefficient and entropy indexes of Bezdek, the Xie-Beni, and exten...

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It...

Medical image segmentation demands a segmentation algorithm which works against noise. The most popular algorithm used in image segmentation is Fuzzy C-Means clustering. It uses only intensity values for clustering which makes it highly sensitive to noise. The comparison of the three fundamental image segmentation methods based on fuzzy logic namely Fuzzy C-Means (FCM), Intuitionistic Fuzzy C-M...