[Retracted] Multidimensional Discrete Big Data Clustering Algorithm Based on Dynamic Grid

Table 1

Clustering process of OptiGrid algorithm.

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Input: data set ; : min-cut-score)
Output: clustering results

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(1) Determine a set of compressed mappings,  = {, ,..., }

(2) Calculate all mappings of dataset ,  ⟶ (D), (D),...,(D)

(3) Initialize the cutting plane list set, best cut⟵, cut⟵

(4) For to do

(A) cut⟵to determine the best local cut (i ())
(B) cut-score⟵Pi () best local cut ( ()) score
(C) add all cutting planes with scores greater than min-cut-score to best-cut

(5) If best cut⟵, then is a cluster

(6) Select cutting planes with the highest score from the best cut, and delete the remaining

(7) A multidimensional grid set is constructed by selecting the optimal cutting plane, and all the points in are mapped to g

(8) Determine the optimal grid in grid set and add it to cluster like set

(9) Check and delete unqualified clusters in cluster collection

(10) For each cluster in do
OptiGrid (, q, min-cut-score)

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