This project presents the implementation of various clustering methods for customer segmentation based on spending behavior. The analysis uses a dataset containing customer income, purchasing activity, and product expenditure to identify distinct customer groups. Several clustering algorithms, including K-Means, K-Median, DBSCAN, Mean Shift, and Fuzzy C-Means, are applied and compared to evaluate their performance. The results show that customers can be grouped into low, medium, and high-value segments, with each method producing different clustering characteristics. This study highlights the effectiveness of clustering techniques in understanding customer behavior and supporting data-driven marketing strategies.

Laporan ini berisi analisis multivariat menggunakan dataset Titanic dengan bantuan software R. Analisis yang dilakukan meliputi pembuatan correlation matrix, variance-covariance matrix, serta perhitungan eigen value dan eigen vector untuk memahami hubungan antar variabel dan tingkat keragaman data.