A streaming data clustering algorithm is presented building upon the density-based self-organizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clus...

Clustering on Distribution measurement is an essential task in mining methodology. The previous methods extend traditional partitioning based clustering methods like k-means and density based clustering methods like DBSCAN rely on geometric measurements between objects. The probability distributions have not been considered in measuring distance similarity between objects. In this paper, object...

Data mining has suit an important in research area because of its ability to get valuable information from the data. The data mining uses various clustering algorithms for grouping related objects. One of the most important clustering algorithm is density based clustering algorithm, which groups the related objects in non linear shapes structure based on the density. But it has the problem of v...

Abstract With the widely application of cluster analysis, number clusters is gradually increasing, as difficulty in selecting judgment indicators numbers. Also, small are crucial to discovering extreme characteristics data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional algorithm based local density (BCALoD) proposed. BCALoD es...