with rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. because o...

We propose a new clustering algorithm satisfying requirements for the post-clustering algorithms as many as possible. The proposed “Fuzzy Concept ART” is the form of combining the concept vector having some advantages in document clustering with Fuzzy ART known as real-time clustering algorithms.

In this paper, we investigate the technique for clustering objects with intuitionistic fuzzy information. We first propose a formula to derive the intuitionistic fuzzy similarity degree between two intuitionistic fuzzy sets and develop an approach to constructing an intuitionistic fuzzy similarity matrix. Then, we present a netting method to make clustering analysis of intuitionistic fuzzy sets...

This paper considers dissimilarity measures and clustering techniques for two special cases of set-defined objects: fuzzy granules and subsequence time series. To deal with clustering of such kind of objects, we propose two implementations that generalize the Fuzzy C-Means algorithm to granular feature spaces. Granular computing is a paradigm oriented towards capturing and processing meaningful...

A hybrid neural network regression models with unsupervised fuzzy clustering is proposed for clustering nonparametric regression models for datasets. In the new formulation, (i) the performance function of the neural network regression models is modified such that the fuzzy clustering weightings can be introduced in these network models; (ii) the errors of these network models are feed-backed i...

Background: In this paper, a generic hesitant fuzzy set (HFS) model for clustering various ECG beats according to weights of attributes is proposed. A comprehensive review of the electrocardiogram signal classification and segmentation methodologies indicates that algorithms which are able to effectively handle the nonstationary and uncertainty of the signals should be used for ECG analysis. Ex...

This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input...

Clustering algorithm is very important for data mining. Fuzzy c-means clustering algorithm is one of the earliest goal-function clustering algorithms, which has achieved much attention. This paper analyzes the lack of fuzzy C-means (FCM) algorithm and genetic clustering algorithm. Propose a hybrid clustering algorithm based on immune single genetic and fuzzy C-means. This algorithm uses the fuz...

Geodemographic analysis has been described as “the analysis of spatially referenced geodemographic and lifestyle data” (See and Openshaw, 2001, p.269) It is widely used in the public and private sectors for the planning and provision of products and services. Geodemographic analysis often uses clustering techniques which are used to classify the geodemographic data into groups, making the data ...