clique算法的基本思路(編輯修改稿)
2024-11-23 03:50 本頁(yè)面
【文章內(nèi)容簡(jiǎn)介】 its, the candidate kdimensional units are determined using candidate generation procedure. ? MDLbased pruning ? To decide which subspaces(and the corresponding dense units) are interesting. ? MDLMinimal Description Length candidate generation procedure ? Input: Dk1, the set of all (k1)dimensional dense unit ? Output: a superset of the set of all kdimensional dense units ? Algorithm: MDLbased pruning ? Coverage of subspace sj ? Sort the subspaces in the descending order of their coverage ? Divide the sorted list of subspaces into two sets: the selected set I and the pruned set P ? How to arrive at the cut point MDLbased pruning ? The code length is minimized to determine the optimal cut point i MDLbased pruning 第二步：識(shí)別聚類(lèi) ? Input: a set of dense units D, all in the same kdimensional space S ? Output:a partition of D into D 1,…,D q,such that all units in D i are connected and no two units u i?D i, u j?D j with i?j are connected. Each such partition is a cluster ? Method: depthfirst search algorithm ? Start with some unit u in D, assign it the first cluster number,and find all the units it is connected
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