tailieunhanh - DRID- A new merging approach

In this paper, we propose an algorithm for merging of cluster. This proposed algorithm merges the clusters which is placed near by to each other because of Cluster balancing is a key factor to achieve good performance. The performance of DRID heavily depends on the dataset availibity and type of environment used by the user. | ISSN:2249-5789 Rimmy Chuchra et al, International Journal of Computer Science & Communication Networks,Vol 2(2), 201-204 DRID- A New Merging Approach Rimmy Chuchra (Computer Science) Lovely Professional University Phagwara, India rimmychuchra01@ ABSTRACT INTRODUCTION Merging of clusters is a deterministic approach which provides results in an efficient manner. It involves the data input values as per the suitability of the algorithm. There are various advantages for merging of clusters like to improve the quality of clusters, to reduce the noise level and to increase the performance of the algorithm. Merging of clusters is possible in any environment it totally depends on the availability of the type of dataset values. In this paper, we propose an algorithm for merging of cluster. This proposed algorithm merges the clusters which is placed near by to each other because of Cluster balancing is a key factor to achieve good performance. The performance of DRID heavily depends on the dataset availibity and type of environment used by the user. The crucial step in this algorithm is how to select the best and next cluster for merging and results and comparisons actually demonstrate that the proposed DRID is an effective approach which helps to reduce execution time and increase the overall performance of the algorithm. Data mining is basically called “sorting technique” which helps to detect patterns which may be hidden or unknown. Generally data mining parameters include path analysis, and classification, association and clustering. Each parameter has one specific goal. The goal of classification is looking for new patterns. The goal of path analysis is to search for those events in which is part of event is happened now and other occur later. The goal of association is looking for those patterns which are actually shows interrelated behaviour. And the goal of clustering is finding those patterns which are previously unknown.