Trend Analysis Using Agglomerative Hierarchical Clustering Approach for Time Series Big Data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Subbulakshmi, P. | - |
dc.contributor.author | Vimal, S. | - |
dc.contributor.author | Kaliappan, M. | - |
dc.contributor.author | Robinson, Y. Harold | - |
dc.contributor.author | Kim, Mucheol | - |
dc.date.accessioned | 2023-02-08T06:41:44Z | - |
dc.date.available | 2023-02-08T06:41:44Z | - |
dc.date.issued | 2021-10 | - |
dc.identifier.issn | 2569-7072 | - |
dc.identifier.issn | 2569-7080 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/60261 | - |
dc.description.abstract | Road traffic accidents are a “global tragedy” that generates unpredictable chunks of data having heterogeneity. To avoid this heterogeneous tragedy, we need to fraternize and categorize the datasets. This can be done with the help of clustering and association rule mining techniques. As the trend of accidents is increasing throughout the year, agglomerative hierarchical clustering approach is proposed for time series big data for trend analysis. This clustering approach segments the time sequence data into different clusters after normalizing the discrete time sequence data. Agglomerative hierarchical clustering takes the objects with similar properties and groups them together to form the group of clusters. The paradigmatic time sequence (PTS) data for each cluster with the help of dynamic time warping (DTW) is identified that calculates the closest time sequence. The PTS analyzes various zone details and forms a cluster to report the data. This approach is more useful and optimal than the traditional statistical techniques. | - |
dc.format.extent | 8 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER INTERNATIONAL PUBLISHING AG | - |
dc.title | Trend Analysis Using Agglomerative Hierarchical Clustering Approach for Time Series Big Data | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/978-3-030-70296-0_67 | - |
dc.identifier.bibliographicCitation | ADVANCES IN ARTIFICIAL INTELLIGENCE AND APPLIED COGNITIVE COMPUTING, pp 869 - 876 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000846871000067 | - |
dc.citation.endPage | 876 | - |
dc.citation.startPage | 869 | - |
dc.citation.title | ADVANCES IN ARTIFICIAL INTELLIGENCE AND APPLIED COGNITIVE COMPUTING | - |
dc.type.docType | Proceedings Paper | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | Big data | - |
dc.subject.keywordAuthor | Agglomerative hierarchical clustering | - |
dc.subject.keywordAuthor | Paradigmatic time series | - |
dc.subject.keywordAuthor | Trend analysis | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Applied | - |
dc.description.journalRegisteredClass | foreign | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang 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.