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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yantao Zhou Zhengguo Wu Xingdong Yi |
| Copyright Year | 2008 |
| Description | Author affiliation: Coll. of Electr. & Inf. Eng., Hunan Univ., Changsha (Yantao Zhou; Xingdong Yi) || Inf. & Electr. Eng. Coll., Naval Eng. Univ., Wuhan (Zhengguo Wu) |
| Abstract | GRPC algorithm (a grid-based clustering algorithm with referential parameters) had put forward by authors of this paper, and the algorithm provided user with a feasible technology for reducing the blindness of parameter assignment. The work of this paper is to develop GRPC algorithm in the aspects of scalability and high-dimensionality. We process large-scale data set by means of random sampling technique and transform the clustering of high-dimensional data into the clustering of two-dimensional data. Experimental results confirmed that this algorithm (EGRPC) could effectively cluster vast data set whose data is three-dimensional or other high-dimensional. |
| Starting Page | 1832 |
| Ending Page | 1837 |
| File Size | 362159 |
| Page Count | 6 |
| File Format | |
| ISBN | 9780769533988 |
| DOI | 10.1109/ICYCS.2008.162 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Shape Scalability referential parameter density threshold Grid Partitioning algorithms Paper technology high dimension Clustering Clustering algorithms Sampling methods Grid computing Large-scale systems Gravity |
| Content Type | Text |
| Resource Type | Article |
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