iVIBRATE: Interactive Visualization-Based Framework for Clustering Large Datasets

Abstract

With continued advances in communication network technology and sensing technology, there is astounding growth in the amount of data produced and made available through cyberspace. Efficient and high-quality clustering of large datasets continues to be one of the most important problems in large-scale data analysis. A commonly used methodology for cluster analysis on large datasets is the three-phase framework of sampling/summarization, iterative cluster analysis, and disk-labeling. There are three known problems with this framework which demand effective solutions. The first problem is how to effectively define and validate irregularly shaped clusters, especially in large datasets. Automated algorithms and statistical methods are typically not effective in handling these particular clusters. The second problem is how to effectively label the entire data on disk (disk-labeling) without introducing additional errors, including the solutions for dealing with outliers, irregular clusters, and cluster boundary extension. The third obstacle is the lack of research about issues related to effectively integrating the three phases. In this article, we describe iVIBRATE—an interactive visualization-based three-phase framework for clustering large datasets. The two main components of iVIBRATE are its VISTA visual cluster-rendering subsystem which invites human interplay into the large-scale iterative clustering process through interactive visualization, and its adaptive ClusterMap labeling subsystem which offers visualization-guided disk-labeling solutions that are effective in dealing with outliers, irregular clusters, and cluster boundary extension. Another important contribution of iVIBRATE development is the identification of the special issues presented in integrating the two components and the sampling approach into a coherent framework, as well as the solutions for improving the reliability of the framework and for minimizing the amount of errors generated within the cluster analysis process. We study the effectiveness of the iVIBRATE framework through a walkthrough example dataset of a million records and we experimentally evaluate the iVIBRATE approach using both real-life and synthetic datasets. Our results show that iVIBRATE can efficiently involve the user in the clustering process and generate high-quality clustering results for large datasets.