{"id":248,"date":"2024-09-12T12:45:31","date_gmt":"2024-09-12T12:45:31","guid":{"rendered":""},"modified":"2024-09-12T12:45:31","modified_gmt":"2024-09-12T12:45:31","slug":"k-means-clustering-algorithm","status":"publish","type":"post","link":"https:\/\/www.upskillcampus.com\/blog\/k-means-clustering-algorithm\/","title":{"rendered":"K means Clustering Algorithm in ML &#8211; Types and Applications"},"content":{"rendered":"<div style=\"background:#edf6ff;border: 1px solid #aaa;border-radius: 4px;box-shadow: 0 1px 1px rgb(0 0 0 \/ 5%);display:table;margin-bottom:1em;padding: 10px;position:relative;width:auto;\">\n<div class=\"btnSHown\" style=\"color:blue;font-size:18px;font-weight:600;cursor:pointer;\n\"><button class=\"btn btn-primary ml-1 mr-2 px-1 py-0\"><img decoding=\"async\" src=\"https:\/\/www.theiotacademy.co\/assets\/images\/socialicons\/bars-solid-icon-new.svg\" style=\"width: 33px;\n    filter: invert(1);\" \/><\/button><span id=\"tbleShowhdd\">Table of Contents [show]<\/span><\/div>\n<nav>\n<ul>\n<li><a class=\"blog-heading_link-c\" href=\"#overview-of-k-means-clustering-algorithm-in-machine-learning\" title=\"1.Overview of K means Clustering Algorithm in Machine Learning\">1. Overview of K means Clustering Algorithm in Machine Learning<\/a><\/li>\n<li><a class=\"blog-heading_link-c\" href=\"#types-of-clustering-k-means-in-machine-learning\" title=\"2.Types of Clustering K Means in Machine Learning\">2. Types of Clustering K Means in Machine Learning<\/a><\/li>\n<li><a class=\"blog-heading_link-c\" href=\"#the-objective-of-k-means-clustering\" title=\"3.The Objective of K Means Clustering\">3. The Objective of K Means Clustering<\/a><\/li>\n<li><a class=\"blog-heading_link-c\" href=\"#how-k-means-clustering-works\" title=\"4.How K Means Clustering Works?\">4. How K Means Clustering Works?<\/a><\/li>\n<\/ul>\n<ul id=\"show-hide-table-cn\" style=\"display: none;\">\n<li><a class=\"blog-heading_link-c\" href=\"#applications-of-k-means-clustering\" title=\"5.Applications of k Means Clustering\">5. Applications of k Means Clustering<\/a><\/li>\n<li><a class=\"blog-heading_link-c\" href=\"#example-of-k-means-clustering\" title=\"6.Example of K Means Clustering\">6. Example of K Means Clustering<\/a><\/li>\n<li><a class=\"blog-heading_link-c\" href=\"#conclusion\" title=\"7.Conclusion\">7. Conclusion<\/a><\/li>\n<\/ul>\n<\/nav>\n<\/div>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">The K-means clustering algorithm is a simple way to group data. It&#39;s like putting similar things together, such as sorting toys into boxes based on color. K-means does something identical with numbers and data points. We&#39;ll learn how it works and try it out with Python. Along with that, we will clear all your doubts regarding this algorithm in machine learning.&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<h2 id=\"overview-of-k-means-clustering-algorithm-in-machine-learning\" style=\"line-height:1.38; margin-top:24px; margin-bottom:8px\">\n<span style=\"font-size:16pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Overview of K means Clustering Algorithm in Machine Learning<\/span><\/span><\/span><\/span><\/span><\/span><\/h2>\n<p style=\"line-height:1.38\">\n<span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">K-means clustering is a simple yet effective technique for grouping similar data points. It&#39;s like sorting toys into boxes based on color. K-means finds groups of data points that are close together. You choose several groups (K), and K-means puts each data point in the closest group. As a result, this helps you find patterns and structures within your data.&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Clustering K means in <a href=\"https:\/\/www.upskillcampus.com\/blog\/machine-learning\">machine learning<\/a> is a simple way to group data. In addition, it works by putting data points into groups based on how close they are to a group&#39;s center. First, it chooses random centers for the groups. Then, it puts each data point in the closest group. After that, it finds new centers for the groups. However, this process repeats until it finds the best groups. You need to know how many groups you want before you start.<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">K-means clustering algorithm is a simple way to group data. However, it has some limitations. Sometimes, it&#39;s hard to decide how many groups (K) to use. It works best when the data is separated, but it struggles when data points overlap. K-means is fast, but it might not find the best groups. It also doesn&#39;t tell you how good the groups are. If you start with different groups, you might get different results. K-means can also be affected by noise in the data. It might get stuck in a bad spot.<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<h3 id=\"types-of-clustering-k-means-in-machine-learning\" style=\"line-height:1.38; margin-top:21px; margin-bottom:5px\">\n<span style=\"font-size:13.999999999999998pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#434343\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Types of Clustering K Means in Machine Learning<\/span><\/span><\/span><\/span><\/span><\/span><\/h3>\n<p style=\"line-height:1.38\">\n<span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Clustering is a way to group similar things. There are two primary ways to do this:<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<ul>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Hierarchical clustering: <\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">It starts with everyone in one big group and then splits them into smaller groups until everyone is in their group.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Partitioning clustering<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">: This is like dividing a class into teams. You start with several teams and then move people around until everyone is on the right team.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Hierarchical clustering can be done in two ways:<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<ul>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Agglomerative clustering:<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\"> This starts with everyone in their group and then combines similar groups.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Divisive clustering: <\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">This starts with everyone in one big group and then splits the group into smaller groups.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Partitioning clustering can also be done in two ways:<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<ul>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">K-means clustering: <\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">This is a popular way to group data. It chooses several groups (K) and then puts each data point in the closest group.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Fuzzy C-means clustering:<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\"> This is similar to K-means, but it allows data points to belong to more than one group at a time.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<h3 id=\"the-objective-of-k-means-clustering\" style=\"line-height:1.38; margin-top:21px; margin-bottom:5px\">\n<span style=\"font-size:13.999999999999998pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#434343\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">The Objective of K Means Clustering<\/span><\/span><\/span><\/span><\/span><\/span><\/h3>\n<p style=\"line-height:1.38\">\n<span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Clustering is a technique used to group similar data points. For example, it&rsquo;s sorting toys into boxes based on color. By grouping data points with common characteristics, clustering helps you identify patterns, trends, and relationships within your data. As a result, this can be useful for tasks like customer segmentation, image analysis, and anomaly detection.<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<h3 id=\"how-k-means-clustering-works\" style=\"line-height:1.38; margin-top:21px; margin-bottom:5px\">\n<span style=\"font-size:13.999999999999998pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#434343\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">How K Means Clustering Works?<\/span><\/span><\/span><\/span><\/span><\/span><\/h3>\n<p style=\"line-height:1.38\">\n<span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">K-means clustering algorithm is an unsupervised learning algorithm that divides a dataset into a pre-defined number of clusters. The goal is to group similar data points and discover underlying patterns or structures within the data.&nbsp;&nbsp;&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">The algorithm works by following these steps:<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<ul>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Initialization:<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\"> Randomly select K data points as initial centroids, which represent the centers of the clusters.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Assignment: <\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Assign each data point to the nearest centroid based on Euclidean distance. This creates initial clusters.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Update:<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\"> Calculate new centroids for each cluster by taking the average of all data points assigned to that cluster.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Reprise:<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\"> Repeat steps 2 and 3 until the centroids no longer change significantly or a maximum number of iterations is reached.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">The final clusters represent groups of similar data points, and the centroids serve as representative points for each cluster.&nbsp;&nbsp;&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Key points to remember:<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<ul>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">The choice of K, the number of clusters, is crucial and can significantly impact the results.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">K-means is sensitive to the initialization of centroids. Different initializations can lead to different clustering results.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">K-means assumes that clusters are spherical and of equal size, which may not always be the case in real-world data.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">For large datasets, the K-means clustering algorithm can be computationally expensive, especially for high-dimensional data.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Despite these limitations, K-means is a popular and widely used clustering algorithm due to its simplicity and efficiency. Moreover, it is often used in various applications, including customer segmentation, image segmentation, and anomaly detection.<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<h3 id=\"applications-of-k-means-clustering\" style=\"line-height:1.38; margin-top:21px; margin-bottom:5px\">\n<span style=\"font-size:13.999999999999998pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#434343\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Applications of K Means Clustering<\/span><\/span><\/span><\/span><\/span><\/span><\/h3>\n<p style=\"line-height:1.38\">\n<span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">K-means clustering algorithm is a simple way to group data. It&#39;s like putting similar things together. It has many uses, like:<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<ul>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Customer groups:<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\"> Banks can group customers based on how they use their accounts. In addition, this helps them offer personalized deals.&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Image parts:<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\"> K-means can find similar parts of an image, which is useful for identifying things in pictures.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Song suggestions: <\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">K-means can suggest songs based on what you like to listen to.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Traffic patterns:<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\"> K-means can find patterns in traffic data to understand where traffic is slow.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Image compression:<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\"> K-means can make images smaller without losing much quality.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Finding fraud:<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\"> K-means can help detect fake activity.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Predicting customer loss: <\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">K-means can help predict when customers might stop using a service.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<li aria-level=\"1\" style=\"list-style-type:disc\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:700\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Finding cybercrime:<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\"> K-means can help find signs of cybercrime.<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<h3 id=\"example-of-k-means-clustering\" style=\"line-height:1.38; margin-top:21px; margin-bottom:5px\">\n<span style=\"font-size:13.999999999999998pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#434343\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Example of K Means Clustering&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><\/h3>\n<p style=\"line-height:1.38\">\n<span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">The following section will elaborate on various examples. Read and go through them.&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">1.&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">sns.set_style(&quot;whitegrid&quot;)<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">g=sns.lineplot(x=range(1,11), y=sse)<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">g.set(xlabel =&quot;Number of cluster (k)&quot;,&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ylabel = &quot;Sum Squared Error&quot;,&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;title =&#39;Elbow Method&#39;)<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">plt.show()<\/span><\/span><\/span><\/span><\/span><\/span><br \/>\n&nbsp;<\/p>\n<p style=\"line-height:1.38\">\n<span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">2.&nbsp; <\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">plt.scatter(X[:,0],X[:,1],c = pred)<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">for i in clusters:<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">&nbsp;&nbsp;&nbsp;&nbsp;center = clusters[i][&#39;center&#39;]<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">&nbsp;&nbsp;&nbsp;&nbsp;plt.scatter(center[0],center[1],marker = &#39;^&#39;,c = &#39;red&#39;)<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">plt.show()<\/span><\/span><\/span><\/span><\/span><\/span><br \/>\n&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">3.&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">def pred_cluster(X, clusters):<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">&nbsp;&nbsp;&nbsp;&nbsp;pred = []<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">&nbsp;&nbsp;&nbsp;&nbsp;for i in range(X.shape[0]):<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dist = []<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;for j in range(k):<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dist.append(distance(X[i],clusters[j][&#39;center&#39;]))<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pred.append(np.argmin(dist))<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"line-height:1.38\"><span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:italic\"><span style=\"text-decoration:none\">&nbsp;&nbsp;&nbsp;&nbsp;return pred&nbsp;&nbsp;&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<h4 id=\"conclusion\" style=\"line-height:1.38; margin-top:19px; margin-bottom:5px\">\n<span style=\"font-size:12pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#666666\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">Conclusion<\/span><\/span><\/span><\/span><\/span><\/span><\/h4>\n<p style=\"line-height:1.38\">\n<span style=\"font-size:11pt; font-variant:normal; white-space:pre-wrap\"><span style=\"font-family:Arial,sans-serif\"><span style=\"color:#000000\"><span style=\"font-weight:400\"><span style=\"font-style:normal\"><span style=\"text-decoration:none\">K-means clustering algorithm is a simple yet effective technique for grouping similar data points. Moreover, it&#39;s a popular unsupervised learning algorithm that has a wide range of applications, from customer segmentation to image analysis. While K-means is easy to understand and implement, it&#39;s important to know its limitations, such as sensitivity to initialization and the assumption of spherical clusters. By understanding these limitations and using K-means appropriately, you can effectively leverage its power to uncover valuable insights from your data.&nbsp;&nbsp;<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<h4>\n<strong>Frequently Asked Questions<\/strong><\/h4>\n<div class=\"inblogffschema-faq\">\n<h5>\n<strong>Q1. What type of data does k-means clustering work best with? <\/strong><\/h5>\n<p><strong>Ans<\/strong>. K-means works best with numbers. If the toys are different sizes, it&#39;s easy to put them in the right boxes. But if the toys are different shapes or colors, it might be harder. So, K-means is best for data that can be measured with numbers.<\/p>\n<p>\n<strong>Q2. What is cluster K-means in Python? <\/strong><\/p>\n<p><strong>Ans<\/strong>. K-means clustering in Python is an unsupervised machine learning algorithm used to partition data into K-distinct clusters based on feature similarity.<\/p>\n<p>&nbsp;<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>In this blog, we explore the K-means clustering algorithm, its types, and applications. Learn how this popular machine learning technique groups data into clusters, enabling insightful data analysis and problem-solving in various industries.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-248","post","type-post","status-publish","format-standard","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>K means Clustering Algorithm in ML - Types and Applications - Latest Insights &amp; Guides | Career Upskilling Blogs<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.upskillcampus.com\/blog\/k-means-clustering-algorithm\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"K means Clustering Algorithm in ML - Types and Applications - Latest Insights &amp; Guides | Career Upskilling Blogs\" \/>\n<meta property=\"og:description\" content=\"In this blog, we explore the K-means clustering algorithm, its types, and applications. 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