Spectral Clustering Algorithm Based on Improved Gaussian Kernel Function and Beetle Antennae Search with Damping Factor

Algorithm 2

SC-DBAS algorithm.

	Input: data set X, number of clusters K, number of iterations of DBAS algorithm N
	Step 1: construct similarity matrix,
	Step 2: construct the degree matrix
	Step 3: construct Laplace matrix
	Step 4: calculate the eigenvector corresponding to the first k minimum eigenvalues of the Laplace matrix which forms the eigenmatrix
	Step 5: normalize the feature matrix to get a new feature matrix
	Step 6: treat each row of the feature matrix as a data point, and randomly initialize a group of cluster centers as an individual
	Step 7: randomly initialize a group of cluster centers as an individual
	Step 8: calculate the fitness of the right antennae and the left antennae of the current individual, where
	Step 9: update individual location information ,
	Step 10: repeat steps 8 and 9 until the maximum number of iterations is reached
	Step 11: according to the cluster center corresponding to the last individual position, the cluster is obtained
	Output: