Due to the extensive use of composites in various industries and the fact that defects reduce ultimate strength and efficiency during operation, detection of failures in composite parts is very important. The aim of this paper is to use Acoustic Emission (AE) non-destructive method in four-point bending test of carbon/epoxy composite to analyze and examine the failure mechanisms. This method is...

In this thesis, we study the problems of K-means clustering and context quantization. The main task of K-means clustering is to partition the training patterns into k distinct groups or clusters that minimize the mean-square-error (MSE) objective function. But the main difficulty of conventional K-means clustering is that its classification performance is highly susceptible to the initialized s...

The mean shift algorithm is a nonparametric clustering technique that does not make assumptions on the number of clusters and on their shapes. It achieves this goal by performing kernel density estimation, and iteratively locating the local maxima of the kernel mixture. The set of points that converge to the same mode defines a cluster. While appealing, the performance of the mean shift algorit...

In this paper, we investigate the use of the mean shift algorithm with respect to speaker clustering. The algorithm is an elegant nonparametric technique that has become very popular in image segmentation, video tracking and other image processing and computer vision tasks. Its primary aim is to detect the modes of the underlying density and consequently merge those observations being attracted...

A Genetic Algorithm for K-Mean Clustering Varsha Singh Asst. Prof. JSSATE, Noida, Uttar Pradesh, India Prof A K Misra Professor, Deptt of CSE, MNNIT Allahabad, Uttar Pradesh, India _________________________________________________________________________________________ Abstract: Clustering techniques have obtained adequate results when are applied to data mining problems. Clustering is the pro...

To devise efficient solutions for approximating a mean partition in consensus clustering, Dimitriadou et al. [3] presented a necessary condition of optimality for a consensus function based on least square distances. We show that their result is pivotal for deriving interesting properties of consensus clustering beyond optimization. For this, we present the necessary condition of optimality in ...

reservoir models are initially generated from estimates of specific rock properties and maps of reservoir heterogeneity. many types of information are used in reservoir model construction. one of the most important sources of information comes from wells, including well logs and core samples. unfortunately well log and core data are local measurements that may not reflect the reservoir behavior...

Clustering is a method which divides data objects into groups based on the information found in data that describes the objects and relationships among them. There are a variety of algorithms have been developed in recent years for solving problems of data clustering. Data clustering algorithms can be either hierarchical or partitioned. Most promising among them are K-means algorithm which is p...

Fuzzy clustering is well known as a robust and efficient way to reduce computation cost to obtain the better results. In the literature, many robust fuzzy clustering models have been presented such as Fuzzy C-Mean (FCM) and Possibilistic C-Mean (PCM), where these methods are Type-I Fuzzy clustering. Type-II Fuzzy sets, on the other hand, can provide better performance than Type-I Fuzzy sets, es...