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Description: The last step in training phase is refinement of the clusters
found above. Although DynamicClustering counters all the
basic k-means disadvantages, setting the intra-cluster similarity
r may require experimentation. Also, a cluster may
have a lot in common with another, i.e., sequences assigned
to it are as close to it as they are to another cluster. There
may also be denser sub-clusters within the larger ones.
Platform: |
Size: 40101 |
Author: yznushuangyu |
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Description: The last step in training phase is refinement of the clusters
found above. Although DynamicClustering counters all the
basic k-means disadvantages, setting the intra-cluster similarity
r may require experimentation. Also, a cluster may
have a lot in common with another, i.e., sequences assigned
to it are as close to it as they are to another cluster. There
may also be denser sub-clusters within the larger ones. -The last step in training phase is refinement of the clustersfound above. Although DynamicClustering counters all thebasic k-means disadvantages, setting the intra-cluster similarityr may require experimentation. Also, a cluster mayhave a lot in common with another, ie, sequences assignedto it are as close to it as they are to another cluster. Theremay also be denser sub-clusters within the larger ones.
Platform: |
Size: 39936 |
Author: yznushuangyu |
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Description: Multiobjective Particle Swarm with Crowding Distance（多目标优化）-Multiobjective Particle Swarm with Crowding Distance (multi-objective optimization)
Platform: |
Size: 35840 |
Author: 梅传根 |
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Description: Adaptive Multi-Objective Particle Swarm Optimizer
Platform: |
Size: 40960 |
Author: 蔡立宗 |
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Description: 多目标粒子群算法程序，感兴趣的可以进去-Multi-objective particle swarm optimization procedures can be of interest to go look at the
Platform: |
Size: 35840 |
Author: 吴金玲 |
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