Summarization and matching of density-based clusters in streaming environments

Author:

Yang Di1,Rundensteiner Elke A.2,Ward Matthew O.2

Affiliation:

1. Oracle Corporation, Oracle Drive Nashua, NH

2. Worcester Polytechnic Institute, Worcester, MA

Abstract

Density-based cluster mining is known to serve a broad range of applications ranging from stock trade analysis to moving object monitoring. Although methods for efficient extraction of density-based clusters have been studied in the literature, the problem of summarizing and matching of such clusters with arbitrary shapes and complex cluster structures remains unsolved. Therefore, the goal of our work is to extend the state-of-art of density-based cluster mining in streams from cluster extraction only to now also support analysis and management of the extracted clusters. Our work solves three major technical challenges. First, we propose a novel multi-resolution cluster summarization method, called Skeletal Grid Summarization (SGS), which captures the key features of density-based clusters, covering both their external shape and internal cluster structures. Second, in order to summarize the extracted clusters in real-time, we present an integrated computation strategy C-SGS, which piggybacks the generation of cluster summarizations within the online clustering process. Lastly, we design a mechanism to efficiently execute cluster matching queries, which identify similar clusters for given cluster of analyst's interest from clusters extracted earlier in the stream history. Our experimental study using real streaming data shows the clear superiority of our proposed methods in both efficiency and effectiveness for cluster summarization and cluster matching queries to other potential alternatives.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 15 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. Ocean: Online Clustering and Evolution Analysis for Dynamic Streaming Data;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

2. DenForest: Enabling Fast Deletion in Incremental Density-Based Clustering over Sliding Windows;Proceedings of the 2022 International Conference on Management of Data;2022-06-10

3. DISC: Density-Based Incremental Clustering by Striding over Streaming Data;2021 IEEE 37th International Conference on Data Engineering (ICDE);2021-04

4. DILOF;Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining;2018-07-19

5. Framework for real-time clustering over sliding windows;Proceedings of the 28th International Conference on Scientific and Statistical Database Management;2016-07-18

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