Open AccessA time efficient and accurate retrieval of range aggregate queries using fuzzy clustering means (FCM) approach
Author(s) -
A. Murugan,
D. Gobinath,
S. Ganesh Kumar,
B. Muruganantham,
Sarubala Velusamy
Publication year - 2020
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v10i1.pp415-420
Subject(s) - computer science , data mining , cluster analysis , partition (number theory) , aggregate (composite) , fuzzy clustering , centroid , variable (mathematics) , machine learning , artificial intelligence , mathematics , materials science , combinatorics , composite material , mathematical analysis
Massive growth in the big data makes difficult to analyse and retrieve the useful information from the set of available data’s. Statistical analysis: Existing approaches cannot guarantee an efficient retrieval of data from the database. In the existing work stratified sampling is used to partition the tables in terms of static variables. However k means clustering algorithm cannot guarantees an efficient retrieval where the choosing centroid in the large volume of data would be difficult. And less knowledge about the static variable might leads to the less efficient partitioning of tables. Findings: This problem is overcome in the proposed methodology by introducing the FCM clustering instead of k means clustering which can cluster the large volume of data which are similar in nature. Stratification problem is overcome by introducing the post stratification approach which will leads to efficient selection of static variable. Improvements: This methodology leads to an efficient retrieval process in terms of user query within less time and more accuracy.