Description: Z-Score Clustering is a clustering technique that uses Z-scores to group similar data points. The Z-score, which measures how many standard deviations a data point is above or below the mean, allows for the normalization of data and facilitates the identification of patterns. In this approach, data is transformed into a common scale, helping to mitigate the impact of differences in measurement units and data scales. This technique is particularly useful in datasets where features may have different ranges and distributions. By applying Z-score clustering, groups of data that share similar characteristics can be identified, which is crucial in exploratory data analysis and model optimization. Additionally, this technique can be combined with clustering algorithms like K-means, where Z-scores are used to determine the proximity of data points to the centroids of the groups. In summary, Z-score clustering is a powerful tool for data segmentation, allowing analysts and data scientists to uncover meaningful patterns and relationships in large volumes of information.
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