A Data Partitioning Approach for Hierarchical Clustering
- Title
- A Data Partitioning Approach for Hierarchical Clustering
- Author
- 김상욱
- Keywords
- Hierarchical clustering; Data partitioning; Parameter-insensitive
- Issue Date
- 2013-01
- Publisher
- ACM New York, NY, USA ⓒ2013
- Citation
- , Page. 1-4
- Abstract
- In this paper, we propose a parameter-insensitive data partitioning approach for Chameleon, a hierarchical clustering algorithm. The proposed method splits a given dataset into every possible number of clusters by using existing algorithms that do allow arbitrary-sized sub-clusters in partitioning. After that, it evaluates the quality of every set of initial sub-clusters by using our measurement function, and decides the optimal set of initial sub-clusters such that they show the highest value of measurement. Finally, it merges these optimal initial sub-clusters repeatedly and produces the final clustering result. We perform extensive experiments, and the results show that the proposed approach is insensitive to parameters and also produces a set of final clusters whose quality is better than the previous one.
- URI
- https://dl.acm.org/citation.cfm?id=2448628https://repository.hanyang.ac.kr/handle/20.500.11754/74784
- ISBN
- 978-1-4503-1958-4
- DOI
- 10.1145/2448556.2448628
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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