@inproceedings{51c4ad442c9c403abfe4d4fb820fc179,
title = "A prediction-based visual approach for cluster exploration and cluster validation by HOV",
abstract = "Predictive knowledge discovery is an important knowledge acquisition method. It is also used in the clustering process of data mining. Visualization is very helpful for high dimensional data analysis, but not precise and this limits its usability in quantitative cluster analysis. In this paper, we adopt a visual technique called HOV3 to explore and verify clustering results with quantified measurements. With the quantified contrast between grouped data distributions produced by HOV3, users can detect clusters and verify their validity efficiently.",
author = "Zhang, {Ke Bing} and Orgun, {Mehmet A.} and Kang Zhang",
year = "2007",
language = "English",
isbn = "9783540749752",
volume = "4702 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "336--349",
editor = "Kok, {Joost N.} and Jacek Koronacki and {de Mantaras}, {Ramon Lopez} and Stan Matwin and Dunja Mladenic and Andrzej Skowron",
booktitle = "Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings",
address = "United States",
note = "11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007 ; Conference date: 17-09-2007 Through 21-09-2007",
}