Research Article
An Efficient MapReduce-Based Apriori-Like Algorithm for Mining Frequent Itemsets from Big Data
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@INPROCEEDINGS{10.1007/978-3-030-06158-6_8, author={Ching-Ming Chao and Po-Zung Chen and Shih-Yang Yang and Cheng-Hung Yen}, title={An Efficient MapReduce-Based Apriori-Like Algorithm for Mining Frequent Itemsets from Big Data}, proceedings={Wireless Internet. 11th EAI International Conference, WiCON 2018, Taipei, Taiwan, October 15-16, 2018, Proceedings}, proceedings_a={WICON}, year={2019}, month={1}, keywords={Data mining Frequent itemsets Big data MapReduce Apriori}, doi={10.1007/978-3-030-06158-6_8} }- Ching-Ming Chao
Po-Zung Chen
Shih-Yang Yang
Cheng-Hung Yen
Year: 2019
An Efficient MapReduce-Based Apriori-Like Algorithm for Mining Frequent Itemsets from Big Data
WICON
Springer
DOI: 10.1007/978-3-030-06158-6_8
Abstract
Data mining can discover valuable information from large amounts of data so as to utilize this information to enhance personal or organizational competitiveness. Apriori is a classic algorithm for mining frequent itemsets. Recently, with rapid growth of the Internet as well as fast development of information and communications technology, the amount of data is augmented in an explosive fashion at a speed of tens of petabytes per day. These rapidly expensive data are characterized by huge amount, high speed, continuous arrival, real-time, and unpredictability. Traditional data mining algorithms are not applicable. Therefore, big data mining has become an important research issue.
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