## How do you validate clustering result?

Dunn index is another internal clustering validation measure which can be computed as follow:

- For each cluster, compute the distance between each of the objects in the cluster and the objects in the other clusters.
- Use the minimum of this pairwise distance as the inter-cluster separation (min.

**What constitutes a good clustering?**

A good clustering method will produce high quality clusters in which: the intra-class (that is, intra intra-cluster) similarity is high. the inter-class similarity is low. The quality of a clustering method is also measured by its ability to discover some or all of the hidden patterns.

**How are cluster validation statistics used in real life?**

This is important to avoid finding patterns in a random data, as well as, in the situation where you want to compare two clustering algorithms. Generally, clustering validation statistics can be categorized into 3 classes (Charrad et al. 2014,Brock et al. (2008), Theodoridis and Koutroumbas (2008)):

### What is the purpose of external clustering validation?

External clustering validation, which consists in comparing the results of a cluster analysis to an externally known result, such as externally provided class labels. Since we know the “true” cluster number in advance, this approach is mainly used for selecting the right clustering algorithm for a specific dataset.

**What do you need to know about clustering?**

Clustering is an unsupervised machine learning method for partitioning dataset into a set of groups or clusters. A big issue is that clustering methods will return clusters even if the data does not contain any clusters.

**What does silhouette width mean in clustering validation?**

Silhouette width can be interpreted as follow: (almost 1) are very well clustered. (around 0) means that the observation lies between two clusters. are probably placed in the wrong cluster. The Dunn index is another internal clustering validation measure which can be computed as follow: