Visual Cluster Analysis in Data Mining
نویسندگان
چکیده
Clustering is a major technique in data mining. However the numeri-cal feedback of clustering algorithms is difficult for user to have an intuitiveoverview of the dataset that they deal with. Visualization has been proven to bevery helpful for high-dimensional data analysis. Therefore it is desirable to in-troduce visualization techniques with user’s domain knowledge into clusteringprocess. Whereas most existing visualization techniques used in clustering areexploration oriented. Inevitably, they are mainly stochastic and subjective in na-ture. In this paper, we introduce an approach called HOV (Hypothesis OrientedVerification and Validation by Visualization), which projects high-dimensionaldata on the 2D space and reflects data distribution based on user hypotheses. Inaddition, HOV enables user to adjust hypotheses iteratively in order to obtainan optimized view. As a result, HOV provides user an efficient and effectivevisualization method to explore cluster information.
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