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首页> 外文期刊>Wireless personal communications: An Internaional Journal >Discovering Frequent Patterns by Constructing Frequent Pattern Network over Data Streams in E-Marketplaces
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Discovering Frequent Patterns by Constructing Frequent Pattern Network over Data Streams in E-Marketplaces

机译：通过在电子市场中的数据流上构建频繁模式网络来发现频繁模式

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摘要

The extracting useful information such as itemsets and frequent patterns from the data becomes very important in terms of marketing strategies and maximizing profit in e-marketplaces. Although existing algorithms mining frequent patterns from the data are useful for persistent databases, they have some limitations of data mining from dynamic data arising from the continuous, unbounded and high speed characteristics of data streams. To identify useful frequent patterns in data streams, this paper proposes a frequent pattern network and a new method for discovering frequent patterns through the approximation of frequency counting on the network. The frequent pattern network, whose vertices and edges represent summarized information of transaction data, provides a user-centered environment based on the process of continuously mining frequent patterns because the proposed network is a small and compact data structure, and flexible for minimum support value. The experimental results show that proposed method is more efficient than FP-growth and Apriori methods, and the discussion of memory usage demonstrates the efficiency of the proposed method.
机译：从营销策略和最大化电子市场利润方面，从数据中提取有用的信息（例如项目集和频繁模式）变得非常重要。尽管现有的从数据中提取频繁模式的算法对于持久性数据库很有用，但是它们仍然具有从动态数据中挖掘数据的局限性，这些动态数据是由数据流的连续，无界和高速特性引起的。为了识别数据流中有用的频繁模式，本文提出了一种频繁模式网络和一种通过对网络上的频率计数进行近似来发现频繁模式的新方法。频繁模式网络的顶点和边缘表示交易数据的摘要信息，它基于连续挖掘频繁模式的过程提供了一个以用户为中心的环境，因为该网络结构小巧紧凑，并且对于最小支持值具有灵活性。实验结果表明，该方法比FP-growth和Apriori方法更有效，并且对内存使用的讨论证明了该方法的有效性。

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