This analysis applies 5 clustering methods, which are K-Means, K-Median, DBSCAN, Mean Shift, and Fuzzy C-Means, to the UCI Red Wine Quality dataset to explore natural groupings based on physicochemical properties. After preprocessing, validation, and clustering, the findings suggest that wine quality forms a continuous spectrum rather than distinct clusters, with alcohol, volatile acidity, and sulphates emerging as the key influencing features.

Dashboard Demo de una muestra de sitios webs

EPI 553 HW 3, reviewing Multiple Linear Regression, Tests of Hypotheses, and Interaction Analysis

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.

This document presents the R code and output for a comparative clustering analysis of 34 Indonesian provinces using K-Means, K-Medoids (PAM), DBSCAN, Mean Shift, and Fuzzy C-Means methods based on 16 socioeconomic indicators.