A Multiscale Approach for Free-Float Bike-Sharing Electronic Fence Location Planning: A Case Study of Shenzhen City

Algorithm 1

The multiscale clustering.

	input: dataset, k_values, eps_values = [10, 25], min_p = 10
	output: best_k, macro_clusters, best_eps (idx), micro_clusters
	for k = k_values
	kmeans = fitckmeans (dataset, k)
	labels = predict (kmeans, dataset)
	evaluation indicator = [WCSS, Silhouette coefficient]
	end
	best_k = k_values(best_k_idx)//choose the best k value
	kmeans = fitckmeans(data, best_k)//k-means macroclustering
	micro_clusters = cell (best_k, 1)
	for cluster_label = 1 : best_k
	for eps = eps_values
	dbscan_labels = dbscan(cluster_data, eps, 10)
	evaluation indicator = [ WCSS, Silhouette coefficient, CH index]
	end
	best_eps = eps_values(best_eps_idx)//choose the best eps value
	dbscan_labels = dbscan(cluster_data, best_eps, 10)//dbscan microclustering
	micro_clusters{cluster_label} = dbscan_labels;
	end