<ul><li>K-means cluster algorithm groups data points into clusters based on similarity.</li><li>Algorithm uses k to represent the number of clusters.</li><li>Real-world examples include customer grouping based on purchase behavior and text categorization.</li><li>Also utilized in fraud detection and asset classification like risk categories.</li><li>The algorithm iterates to find an optimal solution by choosing centroids randomly and updating clusters.</li><li>It calculates distances, assigns points to the nearest centroid, and recalculates centroids.</li><li>Calculating centroids involves finding the mean of data points in a cluster for two-dimensional data.</li><li>Considerations include pre-specifying clusters and sensitivity to initial centroids.</li><li>Practical application involves analyzing house prices using the K-means clustering technique.</li><li>Preprocessing steps include encoding categorical data and handling missing values.</li></ul>

K means cluster algorithm

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