KDD Tutorial: Multivariate Density Estimation and Visual Clustering Audience: The intended audience includes anyone with an interest in data understanding. Any technical background will suffice. No advanced statistical training will be assumed or required. Individuals with no statistical training have enjoyed this course. Tutorial Abstract: Density estimation has long been recognized as an important tool when used with univariate and bivariate data. But the computer revolution of recent years has provided access to data of unprecedented complexity in ever-growing volume. New tools are required to detect and summarize the multivariate structure of these difficult data. This tutorial is derived from the tutorial leader's 1992 text "Multivariate Density Estimation: Theory, Practice, and Visualization." We demonstrate that density estimation retains its explicative power even when applied to trivariate and quadrivariate data and beyond. By presenting the major ideas in the context of the classical histogram, we quickly grasp an understanding of advanced estimators. We develop links between the intuitive histogram and other methods that are more statistically efficient. The theoretical results outlined are those particularly relevant to application and understanding. The focus is on methodology, new ideas, and practical advice. Also, detailed discussions of nonparametric dimension reduction, nonparametric regression, and classification are included. Because visualization is a key element in effective multivariate nonparametric analysis, we begin with that topic. Density estimation is both an exploratory tool as well as a confirmatory methodology. One of the most important and difficult tasks in data mining is clustering. We describe how density estimation can help.