Mining Sequential Patterns for interval based events by applying multiple constraints

Kalaivany M, U. V.
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引用次数: 1

Abstract

Sequential pattern mining finds the frequent subsequences or patterns from the given sequences. TPrefixSpan algorithm finds the relevant frequent patterns from the given sequential patterns formed using interval based events. In our proposed work, we add multiple constraints like item, length and aggregate to the interval based TPrefixSpan algorithm. By adding these constraints the efficiency and effectiveness of the algorithm improves. The proposed constraint based algorithm CTPrefixSpan has been applied to synthetic medical dataset. The algorithm can be applied for stock market analysis, DNA sequences analysis etc.
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通过应用多个约束挖掘基于间隔的事件的顺序模式
序列模式挖掘从给定序列中查找频繁子序列或模式。TPrefixSpan算法从使用基于间隔的事件形成的给定序列模式中找到相关的频繁模式。在我们提出的工作中,我们在基于区间的TPrefixSpan算法中添加了多个约束,如条目、长度和聚合。通过加入这些约束,提高了算法的效率和有效性。提出的基于约束的CTPrefixSpan算法已应用于合成医学数据集。该算法可用于股票市场分析、DNA序列分析等。
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International Journal of Computer Science and Applications
International Journal of Computer Science and Applications Computer Science-Computer Science Applications
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期刊介绍: IJCSA is an international forum for scientists and engineers involved in computer science and its applications to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the IJCSA are selected through rigorous peer review to ensure originality, timeliness, relevance, and readability.
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