Clustering Analysis of Customers Based on Purchasing Patterns with K-Means Clustering
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Abstract
There are various techniques to classify data, one of which is clustering. What distinguishes clustering techniques from classification techniques is that they do not rely on the labels in the dataset. The main purpose of clustering is to divide data into several clusters based on similar characteristics, while Classification Technique is a technique of grouping data based on the similarity of the labels of the data under study. In this study, the dataset was created using secondary data from kaggle. The analysis process begins with data pre-processing to normalize the variables used, followed by the application of the K-Means Clustering method to group customers into several clusters based on the similarity of their purchasing patterns. This research demonstrates the potential of using clustering analysis to improve understanding of customer behavior and develop more effective business strategies.
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