Web搜索引擎查询流中频繁短语挖掘的新方法

Fifth Annual Conference on Communication Networks and Services Research (CNSR '07) Pub Date : 2007-05-14 DOI:10.1109/CNSR.2007.5

M. Barouni-Ebrahimi, A. Ghorbani

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引用次数: 16

摘要

本文引入了一种适合查询流挖掘的新的频率定义概念频率率。给出了一种在线单遍算法OFSD(在线频繁序列发现)，用于挖掘数据流中所有概念频繁序列的集合，这些概念频繁序列的概念频率率满足用户定义的最小频率率。然后基于OFSD算法提取的概念频繁短语集描述短语推荐算法。我们还设计了一个查询推荐算法OQD (Online query Discovery)。OQD与提出的短语推荐算法一起用于比较目的。仿真结果表明，与OQD算法相比，本文提出的短语推荐算法具有较高的效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Novel Approach for Frequent Phrase Mining in Web Search Engine Query Streams

In this paper, conceptual frequency rate, a new frequency definition suitable for query stream mining, is introduced. An online single-pass algorithm called OFSD (Online Frequent Sequence Discovery) is given, to mine the set of all conceptual frequent sequences in a data stream whose conceptual frequency rates satisfy a minimum user defined frequency rate. Phrase recommender algorithm is then described based on the set of conceptual frequent phrases extracted by the OFSD algorithm. We have also designed a query recommender algorithm, OQD (Online Query Discovery). OQD is used for comparison purposes along side the proposed phrase recommender algorithm. Simulation results show the efficiency of the proposed Phrase recommender algorithm compared to OQD.

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Fifth Annual Conference on Communication Networks and Services Research (CNSR '07)

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