Contents
What is intra-cluster similarity?
inter-class and intra-class cluster similarity is a crucial part in clustering. The inter-class cluster show the distance between data point with cluster center, meanwhile intra-class cluster show the distance between the data point of one cluster with the other data point in other cluster.
What is intra clustering?
Intra-cluster distance is the distance among members of a cluster, rather than the distance between two different clusters. A good distance measure will return a small distance between objects that are similar and produce clusters that are tighter, and that are therefore more reliably discriminated from one another.
What is similarity matrix in clustering?
Spectral clustering is a powerful method for finding structure in a dataset through the eigenvectors of a similarity matrix. Its accuracy depends on how the similarity between pairs of data points is defined.
How does data clustering maximize intra-cluster similarity?
Effective clustering maximizes intra-cluster similarities and minimizes inter-cluster similarities (Chen, Han, and Yu 1996). Before defining inter- and intra-cluster similarities, first we must define the similarity between a data pair.
Is the quality of clustering an internal criterion?
This is an internal criterionfor the quality of a clustering. But good scores on an internal criterion do not necessarily translate into good effectiveness in an application. An alternative to internal criteria is direct evaluation in the application of interest.
How is data clustering used in image segmentation?
This technique is used in many fields such as biological data anal- ysis or image segmentation. The aim is to identify groups of data known as clusters, in which the data are similar. Effective clustering maximizes intra-cluster similarities and minimizes inter-cluster similarities (Chen, Han, and Yu 1996).
When to use nmi as a denominator for clustering?
Because NMI is normalized, we can use it to compare clusterings with different numbers of clusters. The particular form of the denominator is chosen because is a tight upper bound on (Exercise 16.7). Thus, NMI is always a number between 0 and 1.