This paper is published in Volume-3, Issue-2, 2017
Area
Data Mining
Author
Monika Aal
Org/Univ
C.U. Shah University, Surendranagar, Gujarat, India
Pub. Date
29 March, 2017
Paper ID
V3I2-1317
Publisher
Keywords
Clustering, Partition Algorithm, K-Means, K-Medoid.

Citationsacebook

IEEE
Monika Aal. A Survey on Partition Based Parallel Data Mining Algorithms for Clustering, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Monika Aal (2017). A Survey on Partition Based Parallel Data Mining Algorithms for Clustering. International Journal of Advance Research, Ideas and Innovations in Technology, 3(2) www.IJARIIT.com.

MLA
Monika Aal. "A Survey on Partition Based Parallel Data Mining Algorithms for Clustering." International Journal of Advance Research, Ideas and Innovations in Technology 3.2 (2017). www.IJARIIT.com.

Abstract

Volumes of data are exploding in both scientific and commercial domains. Data mining techniques that extract information from the huge amount of data have become popular in many applications. Algorithms are designed to analyze those volumes of data automatically inefficient ways so that users can grasp the intrinsic knowledge latent in the data. Clustering is important in data analysis and data mining applications. Clustering is a division of data into a group of similar objects. Each group called a cluster consists of objects that are similar between themselves and dissimilar between comparing to objects of other groups. This paper is aimed to study of all the parallel data mining algorithms based on partition.
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