...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Systems >A new approach to mine frequent patterns using item-transformation methods
【24h】

A new approach to mine frequent patterns using item-transformation methods

机译：一种使用项目转换方法挖掘频繁模式的新方法

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Mining frequent patterns is a fundamental and crucial task in data-mining problems. The algorithms reported in the literature for mining frequent patterns can be classified into two approaches: the candidate generation-and-test approach (for example, the Apriori algorithm) and the pattern-growth approach (such as the FP-growth algorithm). The approaches both suffered from the problems that their speed is slow for large databases. This paper proposes a novel and simple approach, which does not belong to the above two approaches. This approach treats the database as a stream of data and finds frequent patterns by scanning the database only once. In addition, the approach can incrementally mine frequent patterns if the database is updated or inserted subsequently. Three versions of the approach (i.e., mapping-table, transformation-function, and logic-circuit) are provided. The logic-circuit version is the first one that mines frequent patterns by simple logic gates, and the modeling of this version shows its speed is thousands of times faster than that of the FP-growth algorithm. Analyses and simulations of the approach are also performed. Analyses show that the transformation-function version is much better than the Apriori and FP-growth ones in storage complexity. Simulation results show that the mapping-table version is comparable to the FP-growth algorithm in execution time.
机译：在数据挖掘问题中，频繁模式的挖掘是一项基本且至关重要的任务。文献中报道的用于挖掘频繁模式的算法可以分为两种方法：候选生成和测试方法（例如Apriori算法）和模式增长方法（例如FP-增长算法）。对于大型数据库，这两种方法都存在速度慢的问题。本文提出了一种新颖而简单的方法，它不属于上述两种方法。这种方法将数据库视为数据流，并通过仅扫描数据库一次来查找频繁的模式。此外，如果随后更新或插入数据库，则该方法可以逐步挖掘频繁模式。提供了该方法的三种版本（即，映射表，变换功能和逻辑电路）。逻辑电路版本是第一个通过简单逻辑门挖掘频繁模式的版本，该版本的建模显示其速度比FP-growth算法快数千倍。还对该方法进行了分析和模拟。分析表明，转换功能版本在存储复杂度方面比Apriori和FP-growth版本要好得多。仿真结果表明，映射表版本在执行时间上可与FP-growth算法相媲美。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号