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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | YunFeng Chang Yuan Zhao ShengQin Feng |
| Copyright Year | 2011 |
| Description | Author affiliation: College of Science, China Three Gorges University, YiChang, China (YunFeng Chang; Yuan Zhao; ShengQin Feng) |
| Abstract | In this paper, we propose one method to demonstrate the importance and effectiveness of system ingredients with strong similarity in cluster analysis. As a test, we clustered 1578 SSCI journals with three different collections of journal-journal similarities, which are computed from the aggregated journal-journal citation reports of the Institute of Scientific Information (ISI). The statistical properties of the clustering results and the consistency of the results with ISI category demonstrate the importance and efficiency of those predominant system ingredients with strong similariy, and may aid the management of information for increasingly large complex systems analysis. |
| Starting Page | 309 |
| Ending Page | 312 |
| File Size | 177238 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424496655 |
| e-ISBN | 9781424496662 |
| DOI | 10.1109/ICEMMS.2011.6015683 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-08-08 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Measurement Cluster analysis Predominant Clustering methods Similarity Complex networks Educational institutions Probability distribution Complexity theory Physics |
| Content Type | Text |
| Resource Type | Article |
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