A visual method for high-dimensional data cluster exploration

Visualization is helpful for clustering high dimensional data. The goals of visualization in data mining are exploration, confirmation and presentation of the clustering results. However, the most of visual techniques developed for cluster analysis are primarily focused on cluster presentation rather than cluster exploration. Several techniques have been proposed to explore cluster information by visualization, but most of them depend heavily on the individual user's experience. Inevitably, this incurs subjectivity and randomness in the clustering process. In this paper, we employ the statistical features of datasets as predictions to estimate the number of clusters by a visual technique called HOV3. This approach mitigates the problem of the randomness and subjectivity of the user during the process of cluster exploration by other visual techniques. As a result, our approach provides an effective visual method for cluster exploration. © 2009 Springer-Verlag Berlin Heidelberg

A Visual approach for external cluster validation

Visualization can be very powerful in revealing cluster structures. However, directly using visualization techniques to verify the validity of clustering results is still a challenge. This is due to the fact that visual representation lacks precision in contrasting clustering results. To remedy this problem, in this paper we propose a novel approach, which employs a visualization technique called HOV (hypothesis oriented verification and validation by visualization) which offers a tunable measure mechanism to project clustered subsets and non-clustered subsets from a multidimensional space to a 2D plane. By comparing the data distributions of the subsets, users not only have an intuitive visual evaluation but also have a precise evaluation on the consistency of cluster structure by calculating geometrical information of their data distributions.7 page(s