Contents

## What are clusters on a graph?

Graph clustering refers to clustering of data in the form of graphs. Two distinct forms of clustering can be performed on graph data. Vertex clustering seeks to cluster the nodes of the graph into groups of densely connected regions based on either edge weights or edge distances.

### How do you check if data can be clustered?

One way to quickly visualize whether high dimensional data exhibits enough clustering is to use t-Distributed Stochastic Neighbor Embedding (t-SNE). It projects the data to some low dimensional space (e.g. 2D, 3D) and does a pretty good job at keeping cluster structure if any.

#### What are clusters in scatter plots?

What are clusters in scatter plots? Sometimes the data points in a scatter plot form distinct groups. These groups are called clusters.

**How do you measure cluster performance?**

Clustering quality There are majorly two types of measures to assess the clustering performance. (i) Extrinsic Measures which require ground truth labels. Examples are Adjusted Rand index, Fowlkes-Mallows scores, Mutual information based scores, Homogeneity, Completeness and V-measure.

**How can I identify clusters in my data?**

5 Techniques to Identify Clusters In Your Data 1 Cross-Tab. Cross-tabbing is the process of examining more than one variable in the same table or chart (“crossing” them). 2 Cluster Analysis. Cluster analysis groups related items together using different algorithms to identify the “clusters.” 3 Factor Analysis.

## How is distortion curve generated in clustering algorithm?

The strategy of the algorithm is to generate a distortion curve for the input data by running a standard clustering algorithm such as k-means for all values of k between 1 and n, and computing the distortion (described below) of the resulting clustering.

### When to increase the number of clusters in a data set?

In addition, increasing k without penalty will always reduce the amount of error in the resulting clustering, to the extreme case of zero error if each data point is considered its own cluster (i.e., when k equals the number of data points, n ).

#### Can you use community discovery to solve clustering?

“one cannot trivially apply community discovery to solve clustering and vice versa. despite their similarities, there are important diﬀerences in the approaches.