Discovering Interesting Patterns from Hypergraphs

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ACM Transactions on Knowledge Discovery from Data Pub Date : 2023-09-07 DOI:10.1145/3622940

Md. Tanvir Alam, Chowdhury Farhan Ahmed, M. Samiullah, C. Leung

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引用次数: 0

Abstract

A hypergraph is a complex data structure capable of expressing associations among any number of data entities. Overcoming the limitations of traditional graphs, hypergraphs are useful to model real-life problems. Frequent pattern mining is one of the most popular problems in data mining with a lot of applications. To the best of our knowledge, there exists no flexible pattern mining framework for hypergraph databases decomposing associations among data entities. In this article, we propose a flexible and complete framework for mining frequent patterns from a collection of hypergraphs. To discover more interesting patterns beyond the traditional frequent patterns, we propose frameworks for weighted and uncertain hypergraph mining also. We develop three algorithms for mining frequent, weighted, and uncertain hypergraph patterns efficiently by introducing a canonical labeling technique for isomorphic hypergraphs. Extensive experiments have been conducted on real-life hypergraph databases to prove both the effectiveness and efficiency of our proposed frameworks and algorithms.

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从超图中发现有趣的模式

超图是一种复杂的数据结构，能够表达任意数量的数据实体之间的关联。超图克服了传统图的局限性，有助于对现实问题进行建模。频繁模式挖掘是数据挖掘中最常见的问题之一，有很多应用。据我们所知，目前还不存在用于超图数据库分解数据实体之间关联的灵活模式挖掘框架。在本文中，我们提出了一个灵活而完整的框架，用于从超图集合中挖掘频繁模式。为了发现传统频繁模式之外更有趣的模式，我们还提出了加权和不确定超图挖掘的框架。通过引入同构超图的规范标记技术，我们开发了三种有效挖掘频繁、加权和不确定超图模式的算法。在真实的超图数据库上进行了大量的实验，以证明我们提出的框架和算法的有效性和效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.