What is K-Means Clustering?
K-Means Clustering is an unsupervised learning algorithm in machine learning that groups unlabelled data points into clusters based on their similarities or proximity to one another. The objective of K-Means Clustering is to minimise the sum of squared distances between each data point and its assigned centroid or cluster centre. The algorithm is widely used in data mining, pattern recognition, computer vision, and image segmentation applications. Gain further knowledge about the topic covered in this article by checking out the suggested external site. There, you’ll find additional details and a different approach to the topic. K-Means Clustering!
Step 1: Choose the Number K of Clusters
The first step in K-Means Clustering is to determine the number K of clusters that the algorithm should generate. This step is critical as the choice of K affects the quality and accuracy of the clustering. There are several methods to select K, such as the elbow method, silhouette method, and gap statistic method. The elbow method involves plotting the within-cluster sum of squares against K and selecting the K at which the curve bends like an elbow. The silhouette method calculates the average silhouette width of each K and chooses the K with the maximum value. The gap statistic method compares the within-cluster dispersion of the data to a null reference distribution and selects the K with the largest gap statistic.
Step 2: Initialise the Centroids
After selecting K, the second step is to initialise K centroids or cluster centres randomly. The centroids serve as the representative or average point of their respective clusters. Random initialisation means that the position of the centroids is not pre-determined, and their values can change with each run of the algorithm. The random initialisation process can influence the final result of the clustering algorithm.
Step 3: Assign Data Points to the Nearest Centroid
The next step is to assign each data point to its nearest centroid or cluster centre based on its distance or similarity measure. Euclidean distance is the most common distance metric used in K-Means Clustering, but other measures like Manhattan distance, Cosine similarity, and Hamming distance can also be used. The proximity of a data point to a centroid is calculated as the distance between them in the feature space. The data point is assigned to the centroid with the shortest distance or smallest dissimilarity.
Step 4: Update the Centroids
After assigning all data points to their nearest centroids, the next step is to update the centroids’ position by calculating the average position of their assigned data points. This process involves computing the mean or average of the data points in each cluster along each dimension or feature. The mean position becomes the new centroid of the cluster, and the process repeats until convergence or the centroids become stable. Convergence means that the position of the centroids does not change significantly, and the clusters remain the same.
Step 5: Evaluate the Quality of the Clusters
The final step is to evaluate the quality and performance of the clusters generated by the K-Means Clustering algorithm. There are several metrics and measures to assess the quality of the clustering, such as the within-cluster sum of squares, the silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. The within-cluster sum of squares measures the compactness or cohesion of the data points within each cluster. The silhouette coefficient measures the separation or isolation of the clusters from each other. The Calinski-Harabasz index measures the inter-cluster variance and intra-cluster variance. The Davies-Bouldin index measures the average similarity between each cluster and its most similar cluster. A high-quality clustering is one that has low within-cluster variance, high between-cluster variance, and well-separated or distinct clusters. Visit this external resource to get additional information on the topic. https://www.analyticsvidhya.com/blog/2019/08/comprehensive-guide-k-means-clustering/, immerse yourself further in the subject.
Conclusion
K-Means Clustering is a popular and effective unsupervised learning algorithm for data clustering and segmentation. The algorithm involves several steps, such as choosing the number of clusters, initialising the centroids, assigning data points to the nearest centroid, updating the centroids, and evaluating the quality of the clusters. By understanding the steps involved in K-Means Clustering, data scientists and analysts can apply the algorithm to various data sets and optimise the results for specific use cases.
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