Traditional visual data mining relies on visualization techniques to disclose implicit information and relationship among data through utilizing human capability of pattern recognition. As an important step in data clustering, noise removal is a challenging topic as domain-specific noise is not well defined and cannot be removed by generic process of data cleaning. This paper addresses two conjugated and reciprocal issues in the use of visualization in noise removal? choosing appropriate visualization techniques based on data removing methods, and designing processing algorithms that suit visualization. The goal is a synthesis of visualization techniques and data mining methods to enhance the overall performance while reducing the subjective factor in visual mining procedure. A visual data cleaning approach called CLEAN is proposed to assist spatial data clustering in four important aspects: removal of domain-specific noise, visualization of data quality, selection of algorithm parameters, and measurement of noise removing methods on parameter sensitiveness. Experiments show that the visualization models in CLEAN do assist effective discovery of natural spatial clusters in a noisy environment.