A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System

Algorithm 1

Inputs: dataset X, parameter K

Output: Clusters

Step 1: The objects are in membrane for

and object is in membrane ;

Step 2: Compute the Euclidean distance matrix by the rule1;

Step 3: Compute the local densities of the data points by the rule2 and

normalize them using (10) and (11);

Step 4: Calculate and for data point using (12) and (4) in every

membrane , respectively;

Step 5: Calculate for all in membrane and sort them

by descend, and select the top K values as the initial cluster center. So as to

determine the centers of the clusters;

Step 6: Split the membrane to K membranes by the division rules, which membranes can

be number from to ;

Step 7: The clustering centers are put in membranes to , respectively.

Step 8: Assign each remaining point to the membrane with the nearest cluster center;

Step 9: Return the clustering result.