In addition to finding meaningful clusters, centroid-based clustering algorithms such as K-means or mean-shift should ideally find centroids that are valid patterns in the input space, representative of data in their cluster. This is challenging with data having a nonconvex or manifold structure, as with images or text. We introduce a new algorithm, Laplacian K-modes, which naturally combines t...

Data clustering is a process of arranging similar data into groups. Data clustering is a common technique for data analysis and is used in many fields, including data mining, pattern recognition and image analysis. In this paper a hybrid clustering algorithm based on K-mean is described. K-means clustering is a common and simple approach for data clustering but this method has some limitation s...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an important problem in a wide variety of fields. Including data mining, pattern recognition, and bioinformatics. It aims to organize a collection of data items into...

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...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Data mining is a computational intelligence discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decision making. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an impor...

clustering technique is one of the most important techniques of data mining and is the branch of multivariate statistical analysis and a method for grouping similar data in to same clusters. with the databases getting bigger, the researchers try to find efficient and effective clustering methods so that they can make fast and real decisions. thus, in this paper, we proposed an improved ant syst...

In Statistical analysis, missing data is a common problem for data quality. Many real datasets have missing data. Imputation preserves all cases by replacing missing data with a probable value based on other available information. Once all missing values have been imputed, the data set can be analyzed using standard techniques for complete data. This paper aim is to describe the efficient imput...