 # K Means Clustering Algorithm: An Easy Guide in 4 Points

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## Introduction

When Data Analysts are given a data set with specific characteristics and values (like a vector), the task is to categorize those items into groups. An unsupervised learning algorithm is used, called the k-means algorithm, to accomplish this.

## 1) What Is The K Means Clustering Algorithm?

Generally, k means algorithms are deployed to subdivide data points of a dataset into clusters based on nearest mean values. To determine the optimal division of your data points into clusters, such that the distance between points in each cluster is minimized, one can use the k means clustering algorithm.

Clustering is one of the most famous exploratory data analysis techniques used to get an intuition about the data structure. It can be defined as identifying subgroups in the datasets such that data points in the same subgroup (cluster) are very similar. In contrast, data points in different clusters are very different.

The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters). The main idea is to define k centers, one for each cluster. These centers should be placed with subtlety because a different location causes a different result. So, the better choice is to put them far away from each other, as much as possible. The next step is to take each point belonging to a given data set and associate it with the nearest center.

When no data is pending, the first step is completed, and an early group age is done. At this point, we need to re-calculate new k-centroids as the barycenter of the clusters resulting from the previous step. After we have these new k-centroids, a contemporary binding must be done between the same data set points and the nearest new center. A loop is generated. As a result of this loop, we may notice that the k centers change their location step-by-step until no more changes are done, or, in other words, centers do not move anymore. Finally, this algorithm aims at minimizing an objective function know as a squared error function given by:

## 2) What Are The K-means Applications?

The application of clustering in machine learning is very popular and is used in market segmentation, document clustering, image segmentation, image compression, etc. Usually, when we undergo a cluster analysis, the goal is either:

1. Get a meaningful intuition of the structure of the data we’re dealing with.
2. Cluster-then-predict where different models will be built for different subgroups if we believe there is a wide variation in different subgroups’ behaviours.

## 3) What Are The Advantages Of K Means Clustering Algorithms?

• Relatively simple to implement
• Scales to large data sets
• Guarantees convergence
• Can warm-start the positions of centroids
• Easily adapts to new examples
• Generalize clusters of different shapes and sizes, such as elliptical clusters

## 4) What Are The Disadvantages Of K Means Clustering Algorithm?

• Manual Selection
• Dependent on initial values
• Clustering data of varying sizes and densities
• Clustering outliers
• Scaling with a number of dimensions

## Conclusion

K-means clustering is one of the most popular clustering algorithms. Usually, the first thing practitioners apply when solving clustering tasks is to get an idea of the dataset’s structure. The goal of k-means is to group data points into distinct non-overlapping subgroups. It does an excellent job when the clusters have a kind of spherical shape. However, it suffers as the geometric shapes of clusters deviate from spherical shapes.

Moreover, it also doesn’t learn the number of clusters from the data and requires it to be pre-defined. It’s always good to know the assumptions behind algorithms/methods to have a good idea about each technique’s strengths and weaknesses. This will help you decide when to use each form and under what circumstances.

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