About K-means clustering

This article examines the K-means clustering.

Hello!

Today, we’re going to talk about K-Means clustering.

K-means clustering is an algorithm that groups data into K clusters with similar characteristics, a type of unsupervised learning.

We will discuss the K-means clustering below.

principle of operation

central initialization

First, select or randomly assign K cluster centers.

allocation step

Assign each data to the nearest cluster center.

Update Steps

Update the center of each cluster to the average position of the data points in that cluster.

Repeat

Repeat the assignment and update steps until the cluster center remains unchanged.

Key Features

Distance-based clustering

The K-means is a distance-based clustering method that performs clustering using the distance between each data point and the cluster center.

Selection of the number of clusters K

For the K-means, you must specify the number K of clusters. It is important to select the appropriate K value.

Utilization

Customer Segmentation

It is used to divide customers into groups with similar characteristics to establish marketing strategies for each group.

Outlier detection

Data far from cluster centers are likely outliers and can be used to detect them.

Precautions

Sensitive to outliers

Because outliers can distort the position of the cluster center, a response is needed.

Select an initial center

Note the initialization method, as the initial cluster-centric selection may vary the speed and results of convergence.

at the end of the day

K-means clustering is a simple yet effective clustering algorithm that is utilized to identify the characteristics of data and classify them into meaningful groups.

Thank you!