如何在Weka中计算聚类评估的准确性 [英] How to compute accuracy for cluster evaluation in Weka

问题描述

我们如何使用Weka计算群集的准确性?

How do we compute accuracy for clusters using Weka?

我可以使用以下公式:

Accuracy (A) = (tp+tn)/Total # samples

但是在Weka工具的实验输出中，我怎么知道什么是真阳性，假阳性，真阴性和假阴性?

but how can I know what is the true positive, false positive, true negative and false negative in the output of experiment in the Weka tool?

推荐答案

Weka中有几种不同的集群模式:

There are a few different clustering modes in Weka:

使用训练集(默认)::聚类后，Weka将训练实例分类为它开发的聚类，并计算每个聚类中的实例所占的百分比.例如，群集0中的X％和群集1中的Y％，等等.

Use training set (default): After clustering, Weka classifies the training instances into clusters it developed and computes the percentage of instances falling in each cluster. For example, X% in cluster 0 and Y% in cluster 1, etc.

提供的测试集:如果集群表示形式像EM算法一样具有概率，则Weka可以评估单独测试数据上的集群.

Supplied test set: It is possible with Weka to evaluate clusterings on separate test data if the cluster representation is probabilistic like EM algorithm.

使用类进行聚类评估:在这种模式下，Weka首先忽略类属性并生成聚类.在测试过程中，它会根据每个群集中class属性的多数值为群集分配类别标签.最后，计算分类误差并显示相应的混淆矩阵.

Clustering evaluation using classes: In this mode Weka first ignores the class attribute and generates the clustering. During testing, it assigns class labels to the clusters on the basis of the majority value of the class attribute within each cluster. Finally, it computes the classification error and also shows the corresponding confusion matrix.

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