@inproceedings{e504ad16f7eb46239e7fc0f16756529d,
title = "Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis",
abstract = "The goal of clustering in data mining is to distinguish objects into partitions/clusters based on given criteria. Visualization methods and techniques may provide users an intuitively appealing interpretation of cluster structures. Having good visually separated groups of the studied data is beneficial for detecting cluster information as well as refining the membership formation of clusters. In this paper, we propose a novel visual approach called M-mapping, based on the projection technique of HOV3 to achieve the separation of cluster structures. With M-mapping, users can explore visual cluster clues intuitively and validate clusters effectively by matching the geometrical distributions of clustered and non-clustered subsets produced in HOV3.",
author = "Zhang, {Ke Bing} and Orgun, {Mehmet A.} and Kang Zhang",
year = "2007",
language = "English",
isbn = "9783540764137",
volume = "4781 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "285--297",
editor = "Guoping Qui and Clement Leung and Xiangyang Xue and Robert Laurini",
booktitle = "Advances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers",
address = "United States",
note = "9th International Conference on Visual Information Systems, VISUAL 2007 ; Conference date: 28-06-2007 Through 29-06-2007",
}