K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering

Algorithm 1

Input:

   //  the data set of K-line series

   //  Similarity threshold

Output:

   //  the set of clusters

KNNCA Algorithm:

 Assign initial value for parameters: ;

 ;

 ;  //   represents the m-th cluster

 ;

 FOR    TO    DO

  ;

   FOR EACH    IN  

    FOR EACH    IN  

     Get based on formula (15);

     If  ()

      ;

      ;  //   represents the ID of a cluster whose element is most similar to

    End

   End

  IF  ()  THEN

    ;

  ELSE

   ;

   ;

   ;