In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double C...

This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set...

This paper introduces a higher-order Takagi-SugenoKang (TSK) neuro-fuzzy inference system and its applications in medical decision support systems. Different from most TSK fuzzy systems that utilize first-order TSK type fuzzy rules, the proposed system is composed of higherorder TKS fuzzy rules that have functions in their consequent parts of the following type: y = b0 x1 b1 x2 ...xp . The type...

In this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.The proposed method combines the fuzzy C-means clustering method, a recurrent functional neural fuzzy network (RFNFN), and a modified differential evolution.The proposed RFNFN is based on the two backlight factors that can accurately detect the compensat...

In data mining clustering techniques are used to group together the objects showing similar characteristics within the same cluster and the objects demonstrating different characteristics are grouped into clusters. Clustering approaches can be classified into two categories namelyHard clustering and Soft clustering. In hard clustering data is divided into clusters in such a way that each data i...

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were us...

The challenging issue in microarray technique is to analyze and interpret the large volume of data. This can be achieved by clustering techniques in data mining. In hard clustering like hierarchical and k-means clustering techniques, data is divided into distinct clusters, where each data element belongs to exactly one cluster so that the out come of the clustering may not be correct in many ti...

Clustering is a data mining technique of grouping set of data objects into multiple groups or clusters so that objects within the cluster have high similarity, but are very dissimilar to the objects in the other clusters. Fuzzy C-Means is the most widely used method where an element may have partial membership grades in more than one fuzzy cluster. This paper makes use of MATLAB language to pro...

Fuzzy C-means is a widely used clustering algorithm in data mining. Since traditional fuzzy C-means algorithms do not take spatial information into consideration, they often can’t effectively explore geographical data information. So in this paper, we design a Spatial Distance Weighted Fuzzy C-Means algorithm, named as SDWFCM, to deal with this problem. This algorithm can fully use spatial feat...

Fuzzy C-Mean (FCM) is an unsupervised clustering algorithm based on fuzzy set theory that allows an element to belong to more than one cluster. Where fuzzy means “unclear” or “not defined” and c denotes “clustering”. In FCM the number of cluster are randomly selected. [15] FCM is the advanced version of K-means clustering algorithm and doing more work than K-means. K-Means just needs to do a di...