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Does anyone know a good algorithm to perform clustering on both discrete and continuous attributes? I am working on a problem of identifying a group of similar customers and each customer has both discrete and continuous attributes (Think type of customers, amount of revenue generated by this customer, geographic location and etc..)

Traditionally algorithms like K-means or EM work for continuous attributes, what if we have a mix of continuous and discrete attributes?

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The user clustering model plays a significant role in the user evaluation framework. In this section, the coupled user clustering model based on mixed data(continuous and discrete attributes) is illustrated here. This model, respectively, takes into account the discrete data and continuous data generated in learning behaviors and incorporates intercoupled and intercoupled relationships of attributes in user clustering. When compared with traditional algorithms, it captures the hidden user interaction information by fully analyzing mixed data, which improves clustering accuracy.

For more information, refer the following link:

If you wish to know more about What Is K means clustering Algorithm in Python then visit this K Means Clustering Algorithm Tutorial.

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