Dimensionality Reduction for Interactive Visual Clustering
P. Alagambigai, K. Thangavel · IGI Global eBooks · 2011
Visualization techniques could enhance the existing methods for knowledge and data discovery by increasing the user involvement in the interactive process. VISTA, an interactive visual cluster rendering system, is known to be an effective model which allows the user to interactively observe clusters in a series of continuously changing visualizations through visual tuning. Identification of the dominating dimensions for visual tuning and visual distance computation process becomes tedious, when the dimensionality of the dataset increases. One common approach to solve this problem is dimensionality reduction. This chapter compares the performance of three proposed feature selection methods viz., Entropy Weighting Feature Selection, Outlier Score Based Feature Selection and Contribution to the Entropy Based Feature Selection for interactive visual clustering system. The cluster quality of the three feature selection methods is also compared. The experiments are carried out for various datasets of University of California, Irvine (UCI) machine learning data repository.