文件名称:mopsoGECCO.tar
- 所属分类：
- 数值算法/人工智能
- 资源属性：
- [Linux] [C/C++] [源码]
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- 2012-11-26
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- 39kb
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- yznush******
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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.
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.
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