# Why Do We Need Clustering?

## Can we get different results for different runs of K means clustering?

Because the initial centroids are chosen randomly, K-means will likely give different results each time it is run.

Ideally these differences will be slight, but it is still important to run the algorithm several times and choose the result which yields the best clusters.

Do not take your results at face value..

## Why do we need cluster analysis?

Cluster analysis can be a powerful data-mining tool for any organisation that needs to identify discrete groups of customers, sales transactions, or other types of behaviors and things. For example, insurance providers use cluster analysis to detect fraudulent claims, and banks use it for credit scoring.

## What is the purpose of K means clustering?

Introduction to K-means Clustering. K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K.

## What is cluster algorithm?

Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. … Today, we’re going to look at 5 popular clustering algorithms that data scientists need to know and their pros and cons!

## What are the example of clustering?

Connectivity models: for example, hierarchical clustering builds models based on distance connectivity. Centroid models: for example, the k-means algorithm represents each cluster by a single mean vector.

## What is clustering good for?

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 are the major drawbacks of K means clustering?

The most important limitations of Simple k-means are: The user has to specify k (the number of clusters) in the beginning. k-means can only handle numerical data. k-means assumes that we deal with spherical clusters and that each cluster has roughly equal numbers of observations.

## How is cluster analysis done?

Cluster analysis is a multivariate method which aims to classify a sample of subjects (or ob- jects) on the basis of a set of measured variables into a number of different groups such that similar subjects are placed in the same group. … – Agglomerative methods, in which subjects start in their own separate cluster.

## What is cluster profiling?

Profiling involves generating descriptions of the clusters with reference to the input variables you used for the cluster analysis. Profiling acts as a class descriptor for the clusters and will help you to ‘tell a story’ so that you can understand this information and use it across your business.

## What is Cluster Analysis example?

Cluster analysis is also used to group variables into homogeneous and distinct groups. This approach is used, for example, in revising a question- naire on the basis of responses received to a draft of the questionnaire.

## How do you know if cluster is good?

A lower within-cluster variation is an indicator of a good compactness (i.e., a good clustering). The different indices for evaluating the compactness of clusters are base on distance measures such as the cluster-wise within average/median distances between observations.

## Where do we use clustering?

Clustering algorithms are a powerful technique for machine learning on unsupervised data….Here are 7 examples of clustering algorithms in action.Identifying Fake News. … Spam filter. … Marketing and Sales. … Classifying network traffic. … Identifying fraudulent or criminal activity.More items…•

## Why do we use clustering?

Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters.