Summary and Future Directions
In this chapter we make a summary of how to optimize the K-means clustering algorithm based on evolutionary computing. The system is still missing a user interface to handle invalid user input. Parallel coordinates that may be used as a tool to visualize data in high-dimensional spaces is only given...
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| Veröffentlicht in: | Computational Intelligence, Evolutionary Computing and Evolutionary Clustering Algorithms Jg. 1; H. 1; S. 113 - 120 |
|---|---|
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| Format: | Buchkapitel |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
Bentham Science Publishers
01.09.2016
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| Schlagworte: | |
| ISBN: | 1681083000, 9781681083001 |
| Online-Zugang: | Volltext |
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| Abstract | In this chapter we make a summary of how to optimize the K-means
clustering algorithm based on evolutionary computing. The system is still missing a
user interface to handle invalid user input. Parallel coordinates that may be used as a
tool to visualize data in high-dimensional spaces is only given a short introduction. In
addition, Particle Swarm Optimization (PSO) is also mentioned to find global solutions
to optimization problems. |
|---|---|
| AbstractList | In this chapter we make a summary of how to optimize the K-means
clustering algorithm based on evolutionary computing. The system is still missing a
user interface to handle invalid user input. Parallel coordinates that may be used as a
tool to visualize data in high-dimensional spaces is only given a short introduction. In
addition, Particle Swarm Optimization (PSO) is also mentioned to find global solutions
to optimization problems. |
| Author | Kristensen, Terje |
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| Keywords | Clustering metric Overplotting Invalid cluster structures Parallel coordinates PSO |
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clustering algorithm based on evolutionary computing. The system is still missing a
user... |
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