Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
%0 Journal Article
%1 noauthororeditor
%A Gole, Sheela
%A Tidke, Bharat
%D 2015
%J International Journal on Foundations of Computer Science & Technology (IJFCST)
%K Association Big Clustering Data Frequent Itemset MapReduce Mining Rule
%N 3
%P 11
%R 10.5121/ijfcst.2015.5307
%T CLUSTBIGFIM-FREQUENT ITEMSET MINING OF
BIG DATA USING PRE-PROCESSING BASED ON
MAPREDUCE FRAMEWORK
%U https://wireilla.com/papers/ijfcst/V5N3/5315ijfcst07.pdf
%V 5
%X Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
@article{noauthororeditor,
abstract = {Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique. },
added-at = {2023-09-28T15:33:12.000+0200},
author = {Gole, Sheela and Tidke, Bharat},
biburl = {https://www.bibsonomy.org/bibtex/24d4931ebfa234fed0ff209ca6d62ca94/devino},
doi = {10.5121/ijfcst.2015.5307},
interhash = {093be0e2695e1dbac911360145d16df6},
intrahash = {4d4931ebfa234fed0ff209ca6d62ca94},
issn = {ISSN : 1839-7662},
journal = {International Journal on Foundations of Computer Science & Technology (IJFCST)},
keywords = {Association Big Clustering Data Frequent Itemset MapReduce Mining Rule},
month = may,
number = 3,
pages = 11,
timestamp = {2023-09-28T15:33:12.000+0200},
title = {CLUSTBIGFIM-FREQUENT ITEMSET MINING OF
BIG DATA USING PRE-PROCESSING BASED ON
MAPREDUCE FRAMEWORK},
url = {https://wireilla.com/papers/ijfcst/V5N3/5315ijfcst07.pdf},
volume = 5,
year = 2015
}