IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Incremental Hierarchical Clustering for Data Insertion and Its Evaluation

Incremental Hierarchical Clustering for Data Insertion and Its Evaluation
View Sample PDF
Author(s): Kakeru Narita (Kyoto Institute of Technology, Kyoto, Japan), Teruhisa Hochin (Graduate School of Information Science, Kyoto Institute of Technology, Kyoto, Japan), Yoshihiro Hayashi (Nitto Seiko Co., LTD., Kyoto, Japan)and Hiroki Nomiya (Graduate School of Information Science, Kyoto Institute of Technology, Kyoto, Japan)
Copyright: 2020
Volume: 8
Issue: 2
Pages: 22
Source title: International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.2020040101

Purchase

View Incremental Hierarchical Clustering for Data Insertion and Its Evaluation on the publisher's website for pricing and purchasing information.

Abstract

Clustering is employed in various fields. However, the conventional method does not consider changing data. Therefore, if the data is changed, the entire dataset must be re-clustered. This article proposes a clustering method to update the clustering result obtained by a hierarchical clustering method without re-clustering when a point is inserted. This article defines the center and the radius of a cluster and determine the cluster to be inserted. The insertion location is determined by similarity based on the conventional clustering method. this research introduces the concept of outliers and consider creating a cluster caused by the insertion. By examining the multimodality of a cluster, the cluster is divided. In addition, when the number of clusters increases, data points previously inserted are updated by re-insertion. Compared with the conventional method, the experimental results demonstrate that the execution time of the proposed method is significantly smaller and clustering accuracy is comparable for some data.

Related Content

Zachary Estreito, Vinh Le, Frederick C. Harris Jr., Sergiu M. Dascalu. © 2024. 15 pages.
Yogesh M. Kamble, Raj B. Kulkarni. © 2024. 10 pages.
Partha Ghosh, Takaaki Goto, Leena Jana Ghosh, Giridhar Maji, Soumya Sen. © 2024. 15 pages.
Kuo Jong-Yih, Hsieh Ti-Feng, Lin Yu-De, Lin Hui-Chi. © 2024. 17 pages.
Megha Bhushan, Utkarsh Verma, Chetna Garg, Arun Negi. © 2024. 14 pages.
Chase D. Carthen, Araam Zaremehrjardi, Vinh Le, Carlos Cardillo, Scotty Strachan, Alireza Tavakkoli, Frederick C. Harris Jr., Sergiu M. Dascalu. © 2024. 14 pages.
Deepak H. A., Vijayakumar T.. © 2023. 24 pages.
Body Bottom