This paper presents a variation of fuzzy c-means (FCM) algorithm that provides data clustering. The proposed algorithm incorporates the local spatial information in a novel fuzzy way. The new algorithm is called Weighted Fuzzy Local Information C-Means (WFLICM). WFLICM can overcome the disadvantages of the known fuzzy Cmeans algorithm and at the same time enhances the clustering performance. Th...

The inverted pendulum is a highly nonlinear and open loop unstable system. To develop an accurate model of the inverted pendulum, different linear and nonlinear methods of identification will be used. However one of the problems encountered during modeling is the collection of experimental data from the inverted pendulum system. Since the output data from the unstable system does not show enoug...

Data clustering techniques have been applied to extract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success. However, due to the diverse structures and intensive noise, there is no reliable clustering approach can be applied to all gene expression data. In this paper, we aim to the feature of high no...

Content-based image retrieval is one of the techniques of image mining. Content-based image retrieval system (CBIR) has been proposed by the medical community to manage the storage and distribution of images to radiologists, physicians, specialists, clinics, and imaging centers. There are three fundamental steps for Content Based Image Retrieval. They are Visual Feature Extraction, Similarity M...

Data has an important role in all aspects of human life and so analyzing this data for discovering proper knowledge is important. Data mining refers to find useful information (extracting patterns or knowledge) from large amount of data. Clustering is an important data mining technique which aims to divide the data objects into meaningful groups called as clusters. It is the process of grouping...

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...

In this note we formulate image segmentation as a clustering problem. Feature vectors, extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of Kohonen learning vector quantization (LVQ) which integrates the Fuzzy cMeans (FCM) model with the learning rate and updating strategies of the LVQ Is used for this task. This network, which segmen...

In this note we formulate image segmentation as a clustering problem. Feature vectors, extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of Kohonen learning vector quantization (LVQ) which integrates the Fuzzy cMeans (FCM) model with the learning rate and updating strategies of the LVQ Is used for this task. This network, which segmen...

Many image segmentation techniques have been developed over the past two decades for segmenting the images, which help for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing. In this, there is a combined approach for segmenting the image. By using histogram equalization to the input image, from which it gives contrast enhancement ...

This paper presents a novel hybrid data clustering algorithm based on parameter adaptive harmony search algorithm. The recently developed parameter adaptive harmony search algorithm (PAHS) is used to refine the cluster centers, which are further used in initializing Expectation-Maximization clustering algorithm. The optimal number of clusters are determined through four well-known cluster valid...