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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Song Gao Ying Xia |
| Copyright Year | 2006 |
| Description | Author affiliation: Coll. of Comput. Sci. & Technol., Chongqing Univ. of Posts & Telecommun. (Song Gao; Ying Xia) |
| Abstract | Spatial clustering, which groups similar objects based on their distance, connectivity, or their relative density in space, is an important component of spatial data mining. The problem of detecting clusters of points belonging to a spatial point process arises in many applications. One of the challenges in spatial clustering is to find clusters under various cluster number, object distribution as well as multi-density. In this paper, we propose GDCIC, a grid-based density-confidence-interval clustering algorithm for multi-density in large spatial database. By using the technique of confidence limits of the density confidence interval, accurate density estimation in local areas can be produced to form local density thresholds. Local dense areas are distinguished from sparse areas or outliers with the help of these thresholds. An optional procedure is included in GDCIC to optimize the clustering result. The experimental studies on both synthetic and real datasets show its high accuracy and performance over existing algorithms |
| Starting Page | 713 |
| Ending Page | 717 |
| File Size | 496840 |
| Page Count | 5 |
| File Format | |
| ISBN | 0769525288 |
| DOI | 10.1109/ISDA.2006.161 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-10-16 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer science Geographic Information Systems Shape Space technology Clustering algorithms Cities and towns Educational institutions Spatial databases Data mining Information technology |
| Content Type | Text |
| Resource Type | Article |
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