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首页> 外文会议>2015 Global Conference on Communication Technologies >MapReduce based frequent itemset mining algorithm on stream data
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MapReduce based frequent itemset mining algorithm on stream data

机译：基于MapReduce的流数据频繁项集挖掘算法

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摘要

Offers on e-commerce websites have been mostly a decision made by companies for advertising or clearing stocks. KAAL algorithm was used on sample transaction data to generate frequent itemsets. These frequent itemsets will give an idea of offers to be made on purchase of base items. With advent of internet, the amount of data being generated by business processes is growing exponentially. This paper makes use of Hadoop MapReduce framework to generate association rules on transaction data stream. Offers are suggested spontaneously as the frequent itemsets are being generated at runtime. The paper concludes that the execution time has a linear relationship with number of transactions per batch. It was found that increase in stock size did not have much impact on execution time. Execution time is also inversely proportional to number of nodes.
机译：电子商务网站上的报价主要是公司做出的广告或清算股票的决定。 KAAL算法用于样本交易数据以生成频繁项集。这些频繁的项目集将提供有关购买基本项目的要约的想法。随着Internet的出现，业务流程生成的数据量呈指数级增长。本文利用Hadoop MapReduce框架在事务数据流上生成关联规则。在运行时生成频繁项集时，会自动建议提供。本文得出结论，执行时间与每批事务的数量成线性关系。发现库存量的增加对执行时间没有太大影响。执行时间也与节点数成反比。

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