최적에 가까운 군집화를 위한 이단계 방법
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 윤복식 | - |
dc.date.accessioned | 2022-03-14T08:45:02Z | - |
dc.date.available | 2022-03-14T08:45:02Z | - |
dc.date.created | 2022-03-14 | - |
dc.date.issued | 2004 | - |
dc.identifier.issn | 1225-1119 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/26396 | - |
dc.description.abstract | The purpose of clustering is to partition a set of objects into several clusters based on some appropriate similarity measure. In most cases, clustering is considered without any prior information on the number of clusters or the structure of the given data, which makes clustering is one example of very complicated combinatorial optimization problems. In this paper we propose a general-purpose clustering method that can determine the proper number of clusters as well as efficiently carry out clustering analysis for various types of data. The method is composed of two stages. In the first stage, two different hierarchical clustering methods are used to get a reasonably good clustering result, which is improved in the second stage by ASA(accelerated simulated annealing) algorithm equipped with specially designed perturbation schemes. Extensive experimental results are given to demonstrate the apparent usefulness of our ASA clustering method. | - |
dc.publisher | 한국경영과학회 | - |
dc.title | 최적에 가까운 군집화를 위한 이단계 방법 | - |
dc.title.alternative | A Two-Stage Method for Near-Optimal Clustering | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 윤복식 | - |
dc.identifier.bibliographicCitation | 한국경영과학회지, v.29, no.1, pp.43 - 56 | - |
dc.relation.isPartOf | 한국경영과학회지 | - |
dc.citation.title | 한국경영과학회지 | - |
dc.citation.volume | 29 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 43 | - |
dc.citation.endPage | 56 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART001094387 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Clustering | - |
dc.subject.keywordAuthor | Simulated Annealing | - |
dc.subject.keywordAuthor | ASA Clustering Method | - |
dc.subject.keywordAuthor | Hierarchical Clustering | - |
dc.subject.keywordAuthor | Number of Clusters | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
94, Wausan-ro, Mapo-gu, Seoul, 04066, Korea02-320-1314
COPYRIGHT 2020 HONGIK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.