关键词: 局部主方向, 可视聚类分析, 可视分析, 交互方法

Abstract: Clustering can group unlabeled data into different cliques, each of which has a similar structure. However, the existing clustering algorithms cannot provide users with an intuitive impression to the data distribution, especially when data lie on a high-dimensional space. Although dimension reduction is helpful for this issue, the effect of low-dimensional visualization may suffer from data overlapping. This paper proposes an interactive clustering visualization method based on local principal directions of data to solve the problem. Specifically, dimension reduction method is adopted first to give users an initial visualization effect of data, then local principal direction and corresponding frequency histogram are calculated and presented, so that users can understand and utilize the statistical characteristics of the data by looking at the frequency histogram along the local principal direction, and interactively shrink or stretch out the distance between points to separate some seemingly accumulated data. Experiments on artificial and real-world datasets indicate that the proposed method effectively improves the separability of data points in the low-dimensional subspace, provides users with a better visual effect to carry out clustering analysis and further exploration. The method is also useful to reduce the iteration times of a widely-used dimension reduction algorithm yet maintain a competitive clustering performance.

Key words: local principal direction, visual clustering analysis, visual analysis, interactive method