Traditional spatial data are generally high dimensional features, and in the clustering of high dimensional data can be directly applied to data processing because of Dimension effect and the data sparseness problem. For CLIQUE algorithm, which usually have the problem such as prone to non-axis direction of overclustering, boundary judgment of fuzzy clustering and smoothing clustering. In this ...

The weighted fuzzy c-mean clustering algorithm (WFCM) and weighted fuzzy c-mean-adaptive cluster number (WFCM-AC) are extension of traditional fuzzy c-mean algorithm to stream data clustering algorithm. Clusters in WFCM are generated by renewing the centers of weighted cluster by iteration. On the other hand, WFCM-AC generates clusters by applying WFCM on the data & selecting best K± initialize...

Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Th...

Intuitionistic fuzzy sets are generalized fuzzy sets whose elements are characterized by a membership, as well as a non-membership value. The membership value indicates the degree of belongingness, whereas the nonmembership value indicates the degree of non-belongingness of an element to that set. The utility of intuitionistic fuzzy sets theory in computer vision is increasingly becoming appare...

Middle spatial resolution multi-spectral remote sensing image is a kind of color image with low contrast, fuzzy boundaries and informative features. In view of these features, the fuzzy C-means clustering algorithm is an ideal choice for image segmentation. However, fuzzy C-means clustering algorithm requires a pre-specified number of clusters and costs large computation time, which is easy to ...

Traditional approaches for modeling TSK fuzzy rules are trying to adjust the parameters in models, and not considering the training data distribution. Hence it will result in an improper clustering structure, especially, when outliers exist. In this paper, a clustering algorithm termed as Robust Proper Structure Fuzzy Regression Algorithm (RPSFR) is proposed to define fuzzy subspaces in a fuzzy...

Most of the clustering methods used in the clustering of chemical structures such as Ward’s, Group Average, Kmeans and Jarvis-Patrick, are known as hard or crisp as they partition a dataset into strictly disjoint subsets; and thus are not suitable for the clustering of chemical structures exhibiting more than one activity. Although, fuzzy clustering algorithms such as fuzzy cmeans provides an i...

In fuzzy clustering, the fuzzy c-means (FCM) clustering algorithm is the best known and used method. An interesting extension of FCM is the fuzzy ISODATA (FISODATA) algorithm; it updates cluster number during the algorithm. That's why we can have more or less clusters than the initialization step. It's the power of the fuzzy ISODATA algorithm comparing to FCM. The aim of this paper is...