Mining or extracting the knowledge from the large amount of data is known as data mining. Here, the collection of data increases exponentially so that for extracting the efficient data we need good methods in data mining. Data mining analyzes several methods for extracting the data. Clustering is one of the methods for extracting the data from large amount of data. Multiple clustering algorithm...

Incremental K-means and DBSCAN are two very important and popular clustering techniques for today‟s large dynamic databases (Data warehouses, WWW and so on) where data are changed at random fashion. The performance of the incremental K-means and the incremental DBSCAN are different with each other based on their time analysis characteristics. Both algorithms are efficient compare to their exist...

Abstarct— In this paper we have presented a proposed a three step framework,starting with finding the initial clusters and then initalizing initial cluster centers and finially partitioning data into most optimal clusters.we have employed some the most effiecient algorithms like Dbscan and K-Means(XK-Means) and we have tested our approach on iris data set. Keywords— exploratory vector;centroids...

Clustering, Segmenting and tracking multiple humans is a challenging problem in complex situations in which extended occlusion, shadow and/or reflection exists. This method includes two stages, segmentation (detection) and tracking. Human hypotheses are generated by shape analysis of the foreground blobs using human shape model. The segmented human hypotheses are tracked with a Kalman filter wi...

As a density clustering algorithm, DBSCAN can find the denser part of data-centered samples, and generalize the category in which sample is relatively centered. This article analyzes the traditional DBSCAN clustering algorithm and its flaw, and discusses an implementation of a density clustering algorithm based on data partitioning. The algorithm can solve the memory support and I/O consuming p...

The aim of this paper has twofold: i) to explore the fundamental concepts and methods of neighborhood-based cluster analysis with its roots in statistics and decision theory, ii) to provide a compact tool for researchers. Since DBSCAN is the first method which uses the concept of neighborhood and it has many successors, we started our discussion by exploring it. Then we compared some of the suc...

Name disambiguation can occur when one is seeking a list of publications of an author who has used different name variations and when there are multiple other authors with the same name. We present an efficient integrative machine learning framework for solving the name disambiguation problem: a blocking method retrieves candidate classes of authors with similar names and a clustering method, D...

Cluster analysis has been widely used in various disciplines such as pattern recognition, computer vision, and data mining. In this work we investigate the applicability of two spatial clustering algorithms, namely DBSCAN and STING, to a new problem domain: Color segmentation of skin lesion (tumor) images. Automated segmentation is a key step in the computerized analysis of skin lesion images s...

Density-based spatial clustering of applications with noise (DBSCAN) is a density-based clustering algorithm that has the characteristics of being able to discover clusters of any shape, effectively distinguishing noise points and naturally supporting spatial databases. DBSCAN has been widely used in the field of spatial data mining. This paper studies the parallelization design and realization...

Mobile telecommunication sector has been accelerated with GSM 1800 licenses in the Turkey. Since then, churn management has won vital importance for the GSM operators. Customers should have segmented according to their profitability for the churn management. If we know the profitable customer segments, we have chance to keep in hand the most important customers via the suitable promotions and c...