Open AccessA Systematic Algorithm for Data Cluster Using Map-Reduce Approach
Author(s) -
S Kechika.,
B Sapthika.,
B Keerthana.,
S Abinaya.,
A Abdulfaiz.
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit195270
Subject(s) - cluster analysis , computer science , data mining , cure data clustering algorithm , correlation clustering , selection (genetic algorithm) , set (abstract data type) , data stream clustering , process (computing) , canopy clustering algorithm , constrained clustering , feature selection , feature (linguistics) , artificial intelligence , pattern recognition (psychology) , programming language , linguistics , philosophy , operating system
We have been studying the problem clustering data objects as we have implemented a new algorithm called algorithm of clustering data using map reduce approach. In cluster, main part is feature selection which involves in recognition of set of features of a subset, since feature selection is considered as a important process. They also produces the approximate and according requests with the original set of features used in this type of approach. The main concept beyond this paper is to give the outcome of the clustering features. This paper which also gives the knowledge about cluster and it's own process. To processing of large datasets the nature of clustering where some more concepts are more helpful and important in a clustering process. In a clustering methodology where more concepts are very useful. The feature selection algorithm which affects, the entire process of clustering is the map-reduce concept. since, feature selection or extraction which is also used in map-reduce approach. The most desirable component is time complexity where efficiency concerns in this criterion. Here time required to find the effective features, where features of quality subsets is equal to effectiveness. The complexity to find based on this criteria based map-reduce features selection approach, which is proposed and evaluated in this paper.