This paper presents fuzzy clustering algorithms for mixed features of symbolic and fuzzy data. El-Sonbaty and Ismail proposed fuzzy c-means (FCM) clustering for symbolic data and Hathaway et al. proposed FCM for fuzzy data. In this paper we give a modi3ed dissimilarity measure for symbolic and fuzzy data and then give FCM clustering algorithms for these mixed data types. Numerical examples and ...

Soft Clustering plays a very important rule on clustering real world data where a data item contributes to more than one cluster. Fuzzy logic based algorithms are always suitable for performing soft clustering tasks. Fuzzy C Means (FCM) algorithm is a very popular fuzzy logic based algorithm. In case of fuzzy logic based algorithm, the parameter like exponent for the partition matrix that we ha...

In recent past, vector quantization has been observed as an efficient technique for image compression. In general, image compression reduces the number bits required to represent an image. The main significance of image compression is that the quality of the image is preserved. This in turn increases the storage space and thereby the volume of the data that can be stored. Image compression is t...

Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide spread popularity, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation. In this paper, a modified adaptive fuzzy c-means clustering (AFCM) algorithm i...

The image segmentation performance of any clustering algorithm is sensitive to the features used and the types of object in an image, both of which compromise the overall generality of the algorithm. This paper proposes a novel fuzzy image segmentation considering surface characteristics and feature set selection strategy (FISFS) algorithm which addresses these issues. Features that are exploit...

Medical image segmentation is an important tool in viewing and analyzing Magnetic Resonance Images (MRI) and solving variousranges of problems in medical imaging. This paper focuses the new approach to segmentation by clustering the image by Genetic Algorithm based Fuzzy C-means clustering (FCM). First segmentation can be done with the help of FCM. Fuzzy C-means can be used to segment the image...

This paper, presents a new image segmentation method based on Wavelets, Particle Swarm Optimization (PSO) and outlier rejection caused by the membership function of the kernel fuzzy local information c-means (KFLICM) algorithm combined with level set is proposed. The segmentation of Magnetic Resonance (MR) images plays an important role in the computer-aided diagnosis and clinical research, but...

An improved segmentation Fuzzy C-Means algorithm (FCM) is proposed for the image recognition of transmission line insulators. In this paper, the improved Wiener filter algorithm is firstly used to filtrate and recover image in pre-processing. Then, the insulator image is segmented based on the improved algorithm FCM. Finally, the contour of insulator is labelled by using connected component lab...

Flow cytometry (FCM) permits high speed measurement of fluorescence from individual cells flowing in a liquid medium. Cells stained with fluorescent dyes that bind specifically and proportionally to desoxyribonucleic acid (DNA) emit fluorescence that reflects their DNA content. Since neoplastic cells often contain abnormal amounts of DNA, FCM can be used as a rapid means to detect neoplasia. Fl...

The fuzzy C-means (FCM) algorithm has got significant importance due to its unsupervised form of learning and more tolerant to variations and noise as compared to other methods in medical image segmentation. In this paper, we propose a conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm. This is achieved through the incorpor...