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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Raivio, K. Simula, O. Laiho, J. |
| Copyright Year | 2001 |
| Description | Author affiliation: Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Finland (Raivio, K.) |
| Abstract | The self-organizing map (SOM) is an efficient tool for visualization and clustering of multidimensional data. It transforms the input vectors on two-dimensional grid of prototype vectors and orders them. The ordered prototype vectors are easier to visualize and explore than the original data. Mobile networks produce a huge amount of spatiotemporal data. The data consists of parameters of base stations (BS) and quality information of calls. There are two alternatives in starting the data analysis. We can build either a general one-cell-model trained using state vectors from all cells, or a model of the network using state vectors with parameters from all mobile cells. In both methods, further analysis is needed to understand the reasons for various operational states of the entire network. |
| Sponsorship | IEEE Comput. Soc. Tech. Committe on Pattern Anal. & Machine Intelligence (TCPAMI) |
| Starting Page | 457 |
| Ending Page | 464 |
| File Size | 885338 |
| Page Count | 8 |
| File Format | |
| ISBN | 0769511198 |
| DOI | 10.1109/ICDM.2001.989552 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2001-11-29 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Land mobile radio Data mining Clustering algorithms Data visualization Data analysis Multiaccess communication Laboratories Information science Prototypes Data engineering |
| Content Type | Text |
| Resource Type | Article |
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