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Graphical Methods for Influential Data Points in Cluster Analysis
Author(s) -
Jang DaeHeung,
Kim Youngil,
AndersonCook Christine M.
Publication year - 2016
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1744
Subject(s) - cluster analysis , data mining , computer science , cluster (spacecraft) , point (geometry) , mechanism (biology) , process (computing) , point process , data point , artificial intelligence , mathematics , statistics , operating system , philosophy , geometry , epistemology , programming language
In cluster analysis, many numerical measures to detect which data points are influential have been proposed in the past literature. These numerical measures provide only limited information about which data points are influential but fail to reveal deeper relationships between the observations. They describe an overall pattern but fail to provide details about the mechanism that exists among the influential data points. In this paper, several graphical methods are described for detecting this mechanism. In the process, each data point is decomposed to show the pattern, how it influences other observations and the partitioning in cluster analysis. The approach also allows comparison of different clustering methods and how these options impact the relationship between observations. Copyright © 2014 John Wiley & Sons, Ltd.