K-means theory for data analysis

Clustering Analysis is a widely used Analysis Method with many algorithms. Currently, analysis tools such as SAS, Splus, SPSS, and SPSS Modeler support clustering analysis, especially in online game data analysis, the role is still very great, especially when we analyze certain customer groups, exclude interference from human grouping, it is important to objectively and comprehensively display the characteristics of the customer group.

Online Game Players' consumption characteristics, copies of game behavior characteristics, tasks, and interactions), player characteristics of different lifecycles, new players, players retained, lost players, and returning players, etc., are widely used, however, we found that sometimes our division is subjective. For example, specifying group variables and criteria. The extraction and designation of these features often requires a lot of industry experience and a lot of attempts, but we only want to take into account more factors and objective facts when grouping, reducing manual intervention.

Therefore, clustering analysis solves this problem. Let's take out the previous study notes today and talk about K-Means, and talk about other algorithms later, finally, let's focus on the cases.

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