A Survey on Achieving Best Knowledge from Frequent Item set Mining using Fidoop
Author(s) -
S. Sandhya,
P. Rajarajeswari
Publication year - 2017
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/ijca2017915068
Subject(s) - computer science , set (abstract data type) , data science , information retrieval , data mining , programming language
Data mining mostly use for data analysis and identifying frequent dataset. Now a days cloud computing is used for data storage and many other data operations like data mining, data retrieval, data distribution etc. As data increasing very rapidly on server day by day, many complications are introduced. Most common problems are load balancing on server and time optimization. To overcome these limitations parallel frequent dataset mining is very effective method. Fidoop parallel frequent dataset mining algorithm which is based on mapreduce framework helps to improve load balancing and FiDoop-HD, speed up the mining performance for highdimensional data analysis. Fidoop is very efficient and scalable algorithm for large clusters of data.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom