Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets

Author:

Makhalova TatianaORCID,Kuznetsov Sergei O.ORCID,Napoli Amedeo

Abstract

AbstractPattern mining is well established in data mining research, especially for mining binary datasets. Surprisingly, there is much less work about numerical pattern mining and this research area remains under-explored. In this paper we propose Mint, an efficient MDL-based algorithm for mining numerical datasets. The MDL principle is a robust and reliable framework widely used in pattern mining, and as well in subgroup discovery. In Mint we reuse MDL for discovering useful patterns and returning a set of non-redundant overlapping patterns with well-defined boundaries and covering meaningful groups of objects. Mint is not alone in the category of numerical pattern miners based on MDL. In the experiments presented in the paper we show that Mint outperforms competitors among which IPD, RealKrimp, and Slim.

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficiently Mining Closed Interval Patterns with Constraint Programming;Lecture Notes in Computer Science;2024

2. Customer Satisfaction Integration Model of Mobile Communication Service Market Based on Data Mining;2022 International Conference on Knowledge Engineering and Communication Systems (ICKES);2022-12-28

3. Robust subgroup discovery;Data Mining and Knowledge Discovery;2022-08-12

4. The minimum description length principle for pattern mining: a survey;Data Mining and Knowledge Discovery;2022-07-04

5. Summarizing Event Sequence Database into Compact Big Sequence;International Journal of Advanced Computer Science and Applications;2022

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3