| Input: DataSt , , the minimal number of the mutual neighborhood , the deviation factor | | Procedure kDDBSCAN(D, k, n, ) | | = 0 / is cluster id / | | For each point in do | | If ⋅id = UNCLASSIFIED Then | | Calculate the -deviation density Devk / see Definition 4 / | | If Then | | ExpandCluster | | = + 1 | | Else | | ⋅id = OUTLINE | | End If | | End If | | End For | | End Procedure |
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