我正在编写一种遗传算法，尝试选择一组数据点以最大化簇间距离，同时保持两个簇之间的簇内距离较小。
我认为某些聚类有效性度量(例如 Davies-Bouldin 指数)将是一个很好的适应度函数，但我正在努力寻找用伪代码或 Java 代码实现的算法。
谁能帮我解决这个问题？
谢谢。
最佳答案
我已经基于https://en.wikipedia.org/wiki/Davies%E2%80%93Bouldin_index用Python实现了它
def davies_bouldin(X, labels, cluster_ctr):
#get the cluster assignemnts
clusters = set(labels)
#get the number of clusters
num_clusters = len(clusters)
#array to hold the number of items for each cluster, indexed by cluster number
num_items_in_clusters = [0]*num_clusters
#get the number of items for each cluster
for i in range(len(labels)):
num_items_in_clusters[labels[i]] += 1
max_num = -9999
for i in range(num_clusters):
s_i = intra_cluster_dist(X, labels, clusters[i], num_items_in_clusters[i], cluster_ctr[i])
for j in range(num_clusters):
if(i != j):
s_j = intra_cluster_dist(X, labels, clusters[j], num_items_in_clusters[j], cluster_ctr[j])
m_ij = np.linalg.norm(cluster_ctr[clusters[i]]-cluster_ctr[clusters[j]])
r_ij = (s_i + s_j)/m_ij
if(r_ij > max_num):
max_num = r_ij
return max_num
def intra_cluster_dist(X, labels, cluster, num_items_in_cluster, centroid):
total_dist = 0
#for every item in cluster j, compute the distance the the center of cluster j, take average
for k in range(num_items_in_cluster):
dist = np.linalg.norm(X[labels==cluster]-centroid)
total_dist = dist + total_dist
return total_dist/num_items_in_cluster
希望对你有帮助
关于java - Java 中的 Davies-Bouldin 指数，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/4919962/