## What are the examples of clustering?

## What are the examples of clustering?

Here are 7 examples of clustering algorithms in action.

- Identifying Fake News. Fake news is not a new phenomenon, but it is one that is becoming prolific.
- Spam filter.
- Marketing and Sales.
- Classifying network traffic.
- Identifying fraudulent or criminal activity.
- Document analysis.
- Fantasy Football and Sports.

## What is spatial clustering algorithm?

Spatial clustering aims to partition spatial data into a series of meaningful subclasses, called spatial clusters, such that spatial objects in the same cluster are similar to each other, and are dissimilar to those in different clusters.

**Why is clustering used?**

Clustering is an unsupervised machine learning method of identifying and grouping similar data points in larger datasets without concern for the specific outcome. Clustering (sometimes called cluster analysis) is usually used to classify data into structures that are more easily understood and manipulated.

**What is clarans algorithm?**

CLARANS (Clustering Large Applications based on RANdomized Search) is a Data Mining algorithm designed to cluster spatial data. CLARANS is a partitioning method of clustering particularly useful in spatial data mining.

### What is spatial clustering analysis?

Spatial clustering analysis has become common in many fields of research, and is most commonly used in epidemiology and criminology applications. 17) defines a spatial cluster as, ‘a geographically bounded group of occurrences of sufficient size and concentration to be unlikely to have occurred by chance.

### What are clustering algorithms used for?

Clustering or cluster analysis is an unsupervised learning problem. It is often used as a data analysis technique for discovering interesting patterns in data, such as groups of customers based on their behavior. There are many clustering algorithms to choose from and no single best clustering algorithm for all cases.

**Which is an example of a clustering algorithm?**

Figure 1: Example of centroid-based clustering. Density-based clustering connects areas of high example density into clusters. This allows for arbitrary-shaped distributions as long as dense areas can be connected. These algorithms have difficulty with data of varying densities and high dimensions.

**How is hierarchical clustering used in machine learning?**

Hierarchical Clustering is categorised into divisive and agglomerative clustering. Basically, these algorithms have clusters sorted in an order based on the hierarchy in data similarity observations. Divisive Clustering or the top-down approach groups all the data points in a single cluster.

## How is a clustering method used in statistical learning?

A clustering method attempts to group the objects based on the definition of similarity supplied to it. — Page 502, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2016.

## How are centroid models used in clustering algorithms?

Centroid models are iterative clustering algorithms where similarity between data points are derived based on their closeness to the centroid of the cluster. The centroid (centre of the cluster) is formed making sure that the distance of the data points is minimum with the centre.