Many fuzzy clustering based techniques when applied to image segmentation do not incorporate spatial relationships of the pixels, while fuzzy rule-based image segmentation techniques are generally application dependent. Also for most of these techniques, the structure of the membership functions is predefined and parameters have to either automatically or manually derived. This paper addresses ...

In this project the image segmentation using Fuzzy Cmeans algorithm and kernel metric. In FCM algorithm by introducing a trade-off weighted fuzzy factor and kernel metric. This factor depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. So we propose generalised rough set FCM algorithm in order to further enhance its robustness to noise and out...

The well-known generalisation of hard Cmeans (HCM) clustering is fuzzy C-means (FCM) clustering where a weight exponent on each fuzzy membership is introduced as the degree of fuzziness. An alternative generalisation of HCM clustering is proposed in this paper. This is called fuzzy entropy (FE) clustering where a weight factor of the fuzzy entropy function is introduced as the degree of fuzzy e...

Clustering task aims at the unsupervised classification of patterns in different groups. To enhance the quality of results, the emerging swarm-based algorithms now-a-days become an alternative to the conventional clustering methods. In this study, an optimization method based on the swarm intelligence algorithm is proposed for the purpose of clustering. The significance of the proposed algorith...

In Data mining, Fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms in dealing with the challenges posed by large collections of vague and uncertain natural data. This paper reviews concept of fuzzy logic and fuzzy clustering. The classical fuzzy c-means algorithm is presented and its limitations are highlighted. Based on the study of the fuzzy c-means algor...

Underwater images suffers from low illumination and poor contrast due to refractions of light rays and poor visibility. Therefore, underwater image segmentation and object extraction is a difficult task. This paper proposed an efficient and fast underwater image segmentation method using thresholding with class 3 fuzzy Cmeans clustering and CLAHE enhancement method. CLAHE enhancement method is ...

In this paper, we present a channel equalization scheme for time-division multiple access (TDMA) satellite communications with burst digital transmission. We show that each channel states of multipath satellite channel follows a Gaussian distribution, which means a Bayesian equalization can be implemented. The parameters of the Bayesian equalization are determined using an unsupervised clusteri...

Tumor is an uncontrolled growth of tissue in any part of the body. The tumor is of different types and they have different characteristics and different treatment. Normally the anatomy of the brain can be viewed by the MRI scan or CT scan. MRI scanned image is used for the entire process. The MRI scan is more comfortable than any other scans for diagnosis. It will not affect the human body, bec...

Fuzzy clustering algorithms like the popular fuzzy cmeans algorithm (FCM) are frequently used to automatically divide up the data space into fuzzy granules (fuzzy vector quantization). In the context of fuzzy systems, in order to be intuitive and meaningful to the user, the fuzzy membership functions of the used linguistic terms have to fulfill some requirements like boundedness of support or u...

Keyword Extraction is the process of assigning keywords to a document where the important words are selected by the system automatically. This proposed frame work is used to extract the keywords using Fuzzy logic method from Meeting Transcripts. At first, the given input is preprocessed. Subsequently, the preprocessed data will be sent to the features extraction method. In this method three fea...