VRKSHA:一种基于多树序列的季节性模式挖掘新方法

Shadi A. Aljawarneh, V. Radhakrishna, Aravind Cheruvu
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引用次数: 72

摘要

从带有时间戳的时态数据库中挖掘关联模式隐含地与扫描输入数据库的任务相关联。查找itemset的支持需要扫描输入数据库。数据库扫描可以是基于快照的,也可以是基于格的。Jin Soung Yoo和Sashi Sekhar最初提出的相似性分析关联模式挖掘的顺序方法是基于快照数据库扫描。快照数据库扫描包括对多个时隙数据库进行逐时隙扫描。顺序方法的主要限制是需要在内存中保留原始时态数据库以查找项集支持。本文提出了一种新的多树结构VRKSHA,消除了在内存中存储原始时态数据库的需要。其基本思想是生成一个带有时间戳的时间树,并使用这种多树结构来获得给定时隙的时间项集的真正支持。相似时态项集的发现基于查找时态项集与引用之间的距离,并验证计算的距离是否满足指定的用户不相似度阈值。如果在计算项目集w.r.t所有时隙的真正支持之前,不相似条件在任何给定时隙失效,则对模式进行修剪。所建议的顺序方法的优点在于它不需要在内存中保留数据库。通过实例分析,验证了该方法的意义和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VRKSHA: A Novel Multi-Tree Based Sequential Approach for Seasonal Pattern Mining
Mining association patterns from a time-stamped temporal database is implicitly associated with task of scanning input database. Finding supports of itemsets requires scanning the input database. Database scan can be either snapshot or lattice based. Sequential method for similarity profiled association pattern mining originally proposed by Jin Soung Yoo and Sashi Sekhar is based on the snapshot database scan. Snapshot database scan involves scanning multi-time slot database, time slot by time slot. The major limitation of sequential method is the requirement to retain original temporal database in the memory for finding itemset supports. In this paper, a novel multi-tree structure called VRKSHA is proposed that eliminates the need to store the original temporal database in memory. The basic idea is to generate a time stamped temporal tree and use this multi-tree structure to obtain true supports of temporal itemsets for a given time slot. Discovery of similar temporal itemsets is based on finding distance between temporal itemset and reference and validating if the computed distance satisfies specified user dissimilarity threshold. A pattern is pruned if the dissimilarity condition fails at any given time slot well before computing true support of itemset w.r.t all time slots. The advantage of proposed sequential approach is from the fact that it does not require database retention in memory. The case study demonstrating working example proves the significance and efficiency of the proposed approach.
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