A dynamic linkage clustering using KD-tree
Joint Authors
Abu Dalfa, Shadi
Makki, Muhammad
Source
The International Arab Journal of Information Technology
Issue
Vol. 10, Issue 3 (31 May. 2013)9 p.
Publisher
Publication Date
2013-05-31
Country of Publication
Jordan
No. of Pages
9
Main Subjects
Topics
Abstract EN
Some clustering algorithms calculate connectivity of each data point to its cluster by depending on density reachability.
These algorithms can find arbitrarily shaped clusters, but they require parameters that are mostly sensitive to clustering performance.
We develop a new dynamic linkage clustering algorithm using kd-tree.
The proposed algorithm does not require any parameters and does not have a worst-case bound on running time that exists in many similar algorithms in the literature.
Experimental results are shown in this paper to demonstrate the effectiveness of the proposed algorithm.
We compare the proposed algorithm with other famous similar algorithm that is shown in literature.
We present the proposed algorithm and its performance in detail along with promising avenues of future research.
American Psychological Association (APA)
Abu Dalfa, Shadi& Makki, Muhammad. 2013. A dynamic linkage clustering using KD-tree. The International Arab Journal of Information Technology،Vol. 10, no. 3.
https://search.emarefa.net/detail/BIM-311918
Modern Language Association (MLA)
Abu Dalfa, Shadi& Makki, Muhammad. A dynamic linkage clustering using KD-tree. The International Arab Journal of Information Technology Vol. 10, no. 3 (May. 2013).
https://search.emarefa.net/detail/BIM-311918
American Medical Association (AMA)
Abu Dalfa, Shadi& Makki, Muhammad. A dynamic linkage clustering using KD-tree. The International Arab Journal of Information Technology. 2013. Vol. 10, no. 3.
https://search.emarefa.net/detail/BIM-311918
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references.
Record ID
BIM-311918