在Weka中使用Kmeans时,可以在模型的结果输出上调用getAssignments()来获取每个给定实例的集群分配。这是一个(截断的)Jython示例:
>>>import weka.clusterers.SimpleKMeans as kmeans
>>>kmeans.buildClusterer(data)
>>>assignments = kmeans.getAssignments()
>>>assignments
>>>array('i',[14, 16, 0, 0, 0, 0, 16,...])
每个簇编号的索引对应于实例。因此,实例0在集群14中,实例1在集群16中,依此类推。
我的问题是:Xmeans有类似的东西吗?我已经浏览了整个API here并且没有看到类似的内容。
答案 0 :(得分:7)
以下是Weka listserv对我的问题的回复:
"Not as such. But all clusterers have a clusterInstance() method. You can
pass each training instance through the trained clustering model to
obtain the cluster index for each."
这是我对这个建议的Jython实现:
>>> import java.io.FileReader as FileReader
>>> import weka.core.Instances as Instances
>>> import weka.clusterers.XMeans as xmeans
>>> import java.io.BufferedReader as read
>>> import java.io.FileReader
>>> import java.io.File
>>> read = read(FileReader("some arff file"))
>>> data = Instances(read)
>>> file = FileReader("some arff file")
>>> data = Instances(file)
>>> xmeans = xmeans()
>>> xmeans.setMaxNumClusters(100)
>>> xmeans.setMinNumClusters(2)
>>> xmeans.buildClusterer(data)# here's our model
>>> enumerated_instances = data.enumerateInstances() #get the index of each instance
>>> for index, instance in enumerate(enumerated_instances):
cluster_num = xmeans.clusterInstance(instance) #pass each instance through the model
print "instance # ",index,"is in cluster ", cluster_num #pretty print results
instance # 0 is in cluster 1
instance # 1 is in cluster 1
instance # 2 is in cluster 0
instance # 3 is in cluster 0
我将所有这些作为参考,因为可以使用相同的方法来获取任何Weka聚类的结果的聚类分配。