A Top-Down Approach for Hierarchical Cluster Exploration by Visualization

摘要：

With the much increased capability of data collection and storage in the past decade, data miners have to deal with much larger datasets in knowledge discovery tasks. Very large observations may cause traditional clustering methods to break down and not be able to cope with such large volumes of data. To enable data miners effectively detect the hierarchical cluster structure of a very large dataset, we introduce a visualization technique HOV3 to plot the dataset into clear and meaningful subsets by using its statistical summaries. Therefore, data miners can focus on investigating a relatively smaller-sized subset and its nested clusters. In such a way, data miners can explore clusters of any subset and its offspring subsets in a top-down fashion. As a consequence, HOV3 provides data miners an effective method on the exploration of clusters in a hierarchy by visualization.

关键词： Top-down data analysis hierarchical cluster exploration visualization

作者: Ke-Bing Zhang Mehmet A. Orgun Peter A. Busch Abhaya C. Nayak

作者单位: Departmen of Computing Macquarie University Sydney NSW 2109 Australia

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 497-508

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）