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For example: market segmentation, image segmentation, document grouping, anomaly detection or data compression, etc. [K-Means Algorithm-Customer Grouping] Assume that there is a large amount of customer transaction information in the company's CRM system, such as purchase time, purchase amount, purchase frequency, type of purchased goods, etc. Our goal is to segment customers into different groups based on this data in order to better tailor marketing strategies. At this time, the K-Means algorithm can be used to efficiently solve this problem. At
the beginning, we need to extract relevant customer transaction information from Malaysia Phone Number Data the CRM system and prepare the data. The quantity and quality of raw data are critical to the success or failure of the ultimate goal. Then, we need to preprocess the extracted data. Clean the data, deal with missing values, outliers, and possibly normalize or standardize it so that the algorithm can better handle the data. The K-Means algorithm needs to specify the number of clusters K in advance, and we need to choose an appropriate K value. You can try different K values and use factors such as the to make your selection. Then initialize the cluster center. Randomly select K data points as the initial cluster centers, and then use
the selected K values to perform the K-Means algorithm to divide customers into K clusters, with each cluster representing a customer group. The process of executing the K-Means algorithm will involve multiple update iterations. After that, we need to evaluate the clustering results and also analyze each cluster to understand the characteristics of each customer group. This may involve features such as purchasing habits, activity, product preferences, etc. You can also use visualization tools to display the feature distribution of each cluster. Once we
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