High Dimensional Cluster Analysis Using Path Lengths

• 作者: Kevin Mcilhany Stephen Wiggins
• 来源期刊: 数据分析和信息处理(英文)
• 学科: 数学
• 关键词: CLUSTERING PATH LENGTH CONSENSUS N-Dimensional Line of SIGHT
• 年，卷（期）: 2018,（3）
• 页码范围: 93-125
• 页数: 33页
• 分类号: O1

摘要

A hierarchical scheme for clustering data is presented which applies to spaces with a high number of dimensions (). The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering;however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-of-Sight algorithm is also developed for clustering. A test bank of 12 data sets with varying properties is used to expose the strengths and weaknesses of each technique. Finally, a robust clustering technique is discussed based on reaching a consensus among the multiple approaches, overcoming the weaknesses found individually.