A Visual Method for High-Dimensional Data Cluster Exploration
نویسندگان
چکیده
Visualization is helpful for clustering high dimensional data. The goals of visualization in data mining are exploration, confirmation and presentation. However, the most of visual techniques serviced for cluster analysis are focused on cluster presentation rather than cluster exploration. Several techniques are proposed to explore cluster information by visualization, but most of them heavily depend on the individual user’s experience. Inevitably, it 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, HOV. This approach avoids the randomness and subjectivity of the user during the process of cluster exploration by other visual techniques. As a result, it provides an effective visual method for cluster exploration.
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