Who provides real-world examples of K-means clustering?
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K-means clustering is a popular unsupervised learning algorithm used to classify data into a fixed number of clusters based on the data’s distance between its points in a two-dimensional space. A good example of K-means clustering would be identifying customer segments, such as high-spenders, average spenders, or low-spenders. You can find online K-means clustering examples on Google and other platforms. This example is a classic application of K-means clustering, where data points representing a variety of products are partition
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Who provides real-world examples of K-means clustering? It’s an AI company named Turi. K-means clustering is one of the key clustering algorithms. It is an unsupervised algorithm, meaning it can work without any prior information. The algorithm finds clusters in a dataset by means of grouping observations together that are close to one another on certain dimensions (called features). It does this by iteratively dividing the data into subsets until the distance between clusters converges, which usually happens after several iterations. It has wide applications in a variety of fields,
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“K-means clustering is an unsupervised machine learning technique that involves assigning points to clusters based on their similarity using the k-means algorithm. Here, k is the number of clusters to assign each point, and it’s important to consider that the clustering results can vary with the input data. So, the algorithm works by first creating an initial set of centroids, which are the mean values of each data point. For each input data point, it searches for the nearest centroid until the input data point is assigned to its cluster. Once the algorithm has
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I have been working in the field of computer vision for more than 5 years and have been using K-means clustering for image and video data analysis. Here are some real-world examples of K-means clustering: 1. Image and video recognition One of the most common applications of K-means clustering is for image and video recognition. The idea is to find clusters of objects in an image or video. Suppose you have a dataset with millions of images or videos, and you want to identify the specific objects in the images. K-means
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Who provides real-world examples of K-means clustering? There are many experts in this field who provide real-world examples of K-means clustering. 1. The IBM Research Group: This is one of the largest research groups in the world for Big Data. see here They have developed a practical K-means clustering method for supervised learning that improves accuracy and precision by several times. The method can handle large and complex datasets. 2. Facebook: This social network giant uses K-means clustering to segment its users
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K-means clustering is one of the most commonly used algorithms in data mining. It is known for its excellent performance in classification and segmentation tasks. Here are a few real-world examples of its application: 1. Credit Card Fraud Detection: K-means clustering is widely used for detecting credit card fraud. By grouping similar transactions, it helps to identify fraudulent transactions and protect the financial system from the same. 2. Gene Expression Analysis: It is widely used in cancer research, where clusters of genes
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Real-world examples of K-means clustering are used in data mining, image analysis, and image segmentation, such as in medical image analysis, in which clusters are defined based on the distribution of pixel values in brain MRIs. K-means clustering is used in image segmentation by identifying regions of interest (ROI) in medical images, such as tumors or blood vessels, and then creating clusters of ROIs that share a similar appearance. In data mining, K-means clustering is used to identify patterns in social networks, such as view website