On exploiting the power of time in data mining

Author:

Böttcher Mirko1,Höppner Frank2,Spiliopoulou Myra1

Affiliation:

1. University of Magdeburg, Magdeburg, Germany

2. University of Applied Sciences, Wolfsbüttel, Germany

Abstract

We introduce the new paradigm of Change Mining as data mining over a volatile, evolving world with the objective of understanding change. While there is much work on incremental mining and stream mining, both focussing on the adaptation of patterns to a changing data distribution, Change Mining concentrates on understanding the changes themselves. This includes detecting when change occurs in the population under observation, describing the change, predicting change and pro-acting towards it. We identify the main tasks of Change Mining and discuss to what extent they are already present in related research areas. We elaborate on research results that can contribute to these tasks, giving a brief overview of the current state of the art and identifying open areas and challenges for the new research area.

Publisher

Association for Computing Machinery (ACM)

Cited by 42 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. System Network Analytics: Evolution and Stable Rules of a State Series;2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA);2022-10-13

2. Call Graph Evolution Analytics over a Version Series of an Evolving Software System;Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering;2022-10-10

3. Strategic planning support for road safety measures based on accident data mining;IATSS Research;2022-10

4. Change-Detection Machine Learning Model for Educational Management;Cybernetics and Systems;2022-05-30

5. Identifying Non-intuitive Relationships Within Returns Data of a Furniture Online-Shop Using Temporal Data Mining;Recent Challenges in Intelligent Information and Database Systems;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