Distributed Data Clustering
Harshit Aggarwal, Akanksha Garg, Kumar Saumay, Dinesh Chandra Maurya
Data Mining, Peer-to-peer, Sensor network, Multi dimensional data.
Cluster analysis is one of the most common techniques in data mining. Distributed computing plays an important role in the Data Mining process. Data Mining often requires huge amounts of resources in storage space and computation time. Clustering is a task of grouping a set of objects in such a way that objects are in the same group. Data mining is a function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for the data in each case. In such systems, each node holds some data value that is local sensor, to concise picture of the global system state needs to be obtained. The sensor network needs to be done without collecting all the data at any location i.e. distributed data. This paper compares the performance of the distributed clustering and to introduce the advance communication in data mining. The number of distributed information sources accessible to a seeker has grown rapidly. Many researchers have proposed clustering algorithms which work efficiently in the distributed mining. In this research paper we have to discuss the comparative analysis of some of these distributed clustering on various parameters.
Article Details
Unique Paper ID: 144450

Publication Volume & Issue: Volume 3, Issue 11

Page(s): 254 - 257
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