Acta Scientiarum Naturalium Universitatis Pekinensis

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Mining Maximal Moving Sequential Patterns in Mobile Environment

MA Shuai, TANG Shiwei, YANG Dongqing, WANG Tengjiao, GAO Jun   

  1. Department of Computer Science, Peking University, Beijing, 100871
  • Received:2003-11-17 Online:2004-05-20 Published:2004-05-20


马帅, 唐世渭, 杨冬青, 王腾蛟, 高军   

  1. 北京大学 计算机科学技术系, 北京,100871

Abstract: Mining moving sequential patterns has great significance for effective and efficient location management in wireless communication systems. Mining moving sequential patterns is different from mining conventional sequential patterns, firstly it needs to consider much about the time factor in moving sequences; secondly it cares about what the next moving is for mobile user, so items must be successive in mining moving sequential patterns. A novel technique to mine moving sequential patterns is proposed. A clustering method is introduced to preprocess the original moving histories into moving sequences, whose main role is to discretize the time attribute. And then an efficient method, called PrefixTree, is presented to mine the moving sequences. Performance study shows that PrefixTree outperforms Revised PrefixSpan-2, which is revised to mine moving sequences, in mining large moving sequence databases.

Key words: clustering, sequential pattern mining, moving sequential pattern mining

摘要: 在移动通信环境中,移动序列模式挖掘对于有效的提高位置管理的服务质量具有重大的意义。移动序列模式挖掘和传统的序列模式挖掘是不同的,首先,前者需要考虑更多的时间因素;其次,移动序列模式中的项之间是连续的,因为关心移动用户的下一次移动情况。本文提出了一种挖掘移动序列模式的新技术:聚类的思想引入到移动序列模式挖掘来处理移动历史的时间离散化,并且提出了一个高效的PrefixTree算法来挖掘移动序列。性能研究表明,PrefixTree算法优于PrefixSpan-2算法。

关键词: 聚类, 序列模式挖掘, 移动序列模式挖掘

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