X-FSPMiner：频繁相似模式挖掘的新算法

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Knowledge Discovery from Data Pub Date : 2024-01-30 DOI:10.1145/3643820

Ansel Y. Rodríguez-González, Ramón Aranda, Miguel Á. Álvarez-Carmona, Angel Díaz-Pacheco, Rosa María Valdovinos Rosas

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引用次数: 0

摘要

频繁相似模式挖掘（FSP 挖掘）可以发现隐藏在经典方法中的频繁模式。然而，使用相似性函数意味着更多的计算工作，因此有必要开发更高效的 FSP 挖掘算法。这项工作旨在提高使用布尔和非递增单调相似函数挖掘所有 FSP 的效率。本文提出了一种用于压缩对象描述集合的数据结构，命名为 FV-Tree，以及一种从 FV-Tree 中挖掘所有 FSP 的算法，命名为 X-FSPMiner。实验结果表明，新算法 X-FSPMiner 在使用布尔和非递增单调相似函数挖掘所有 FSP 方面大大优于最先进的算法。

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X-FSPMiner: A Novel Algorithm for Frequent Similar Pattern Mining

Frequent similar pattern mining (FSP mining) allows found frequent patterns hidden from the classical approach. However, the use of similarity functions implies more computational effort, becoming necessary to develop more efficient algorithms for FSP mining. This work aims to improve the efficiency of mining all FSPs when using Boolean and non-increasing monotonic similarity functions. A data structure to condense an object description collection named FV-Tree, and an algorithm for mine all FSP from the FV-Tree, named X-FSPMiner, are proposed. The experimental results reveal that the novel algorithm X-FSPMiner vastly outperforms the state-of-the-art algorithms for mine all FSP using Boolean and non-increasing monotonic similarity functions.

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来源期刊

ACM Transactions on Knowledge Discovery from Data COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

6.70

自引率

5.60%

发文量

172

审稿时长

3 months

期刊介绍： TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but are not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.