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K-means is a data clustering approach for unsupervised machine learning that can separate unlabeled data into a predetermined number of disjoint groups of equal variance.<br>https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/<br>
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K-Means Algorithm In Machine Learning https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
Table Of Content 1. What Is a K-Means Algorithm? 2. Why Is the K-Means Algorithm Important? 3. How Does K-Means Algorithm Work? 4. Applications Of K-Means Algorithm https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
What Is a K-Means Algorithm? Unlabeled data can be separated into a specified number of disjoint sets of similar variance or clusters using the data clustering technique K-means for unsupervised machine learning. Due to its clarity of use and speed on huge datasets, it is a well-liked algorithm. https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
Why Is the K-Means Algorithm Important? The K-means clustering algorithm is used to locate groups in the data that have not been explicitly labeled. This can be used to confirm organizational hypotheses about the kinds of existing groups or to find unknown groupings in large, complex data sets. https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
How Does K-Means Algorithm Work? K-means is a centroid-based clustering algorithm where per data point is assigned to a cluster based on the distance between it and a centroid. Finding the K number of groups in the dataset is the objective. https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
Applications Of K-Means Algorithm 1. 2. 3. 4. 5. 6. Image Reduction Marketing Division Document Grouping Segmenting Customers Trend Analysis With Dynamic Data Segmentation of Images https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
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