Contents
Which is the best measure of clustering quality?
In general, a measure Q on clustering quality is effective if it satisfies the following four essential criteria: Cluster homogeneity. This requires that the more pure the clusters in a clustering are, the better the clustering. Suppose that ground truth says that the objects in a data set, D, can belong to categories L1, …, Ln.
What is the property of the clustering algorithm?
The clustering algorithm has one important property. For k = 2 if I feed N data points without any significant distinction among them to this algorithm as a result I will get one cluster containing N − 1 data points and one cluster with 1 data point. Obviously this is not what I want.
What do you need to know about cluster completeness?
Cluster completeness requires that a clustering should assign objects belonging to the same category (according to ground truth) to the same cluster. Consider clustering C 1, which contains clusters C1 and C2, of which the members belong to the same category according to ground truth.
How to know if your data is worth clustering?
But before that you should determine if your data has any so called Clustering Tendency i.e. if it is worth clustering and optimum number of clusters: S. Saitta, B. Raphael, and I. F. C. Smith. A comprehensive validity index for clustering. Intell. Data Anal., 12 (6):p. 529–548, 2008.
What makes an extrinsic method effective for clustering?
Whether an extrinsic method is effective largely depends on the measure, Q, it uses. In general, a measure Q on clustering quality is effective if it satisfies the following four essential criteria: Cluster homogeneity. This requires that the more pure the clusters in a clustering are, the better the clustering.
How to assign a score to a clustering?
When the ground truth is available, we can compare it with a clustering to assess the clustering. Thus, the core task in extrinsic methods is to assign a score, Q ( C, C g), to a clustering, C, given the ground truth, C g.
How are cohesion and separation measured in clusters?
A proximity graph based approach can also be used for cohesion and separation. Cluster cohesion is the sum of the weight of all links within a cluster. Cluster separation is the sum of the weights between nodes in the cluster and nodes outside the cluster. Internal Measures: Cohesion and Separation.