Efficient mining of frequent itemsets in social network data based on MapReduce framework
Author:
Affiliation:
1. York University, Toronto, Canada
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/2492517.2500301
Reference22 articles.
1. Toward terabyte pattern mining
2. A sampling-based framework for parallel data mining
3. Parallel leap: large-scale maximal pattern mining in a distributed environment
Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. New Spark solutions for distributed frequent itemset and association rule mining algorithms;Cluster Computing;2023-04-30
2. Design and Implementation of High Value Itemsets from Online Trade;Micro-Electronics and Telecommunication Engineering;2021
3. Frequent Itemsets Based Partitioning Approach to Decision Tree Classifier;Mining Intelligence and Knowledge Exploration;2020
4. Efficient Large Scale Frequent Itemset Mining with Hybrid Partitioning Approach;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2019-03-15
5. Adaptive-Miner: an efficient distributed association rule mining algorithm on Spark;Journal of Big Data;2018-02-20
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3