@inproceedings{900881428c0446e0a10d729586947353,
title = "HOV3: An approach to visual cluster analysis",
abstract = "Clustering is a major technique in data mining. However the numerical feedback of clustering algorithms is difficult for user to have an intuitive overview of the dataset that they deal with. Visualization has been proven to be very helpful for high-dimensional data analysis. Therefore it is desirable to introduce visualization techniques with user's domain knowledge into clustering process. Whereas most existing visualization techniques used in clustering are exploration oriented. Inevitably, they are mainly stochastic and subjective in nature. In this paper, we introduce an approach called HOV3 (Hypothesis Oriented Verification and Validation by Visualization), which projects high-dimensional data on the 2D space and reflects data distribution based on user hypotheses. In addition, HOV3 enables user to adjust hypotheses iteratively in order to obtain an optimized view. As a result, HOV3 provides user an efficient and effective visualization method to explore cluster information.",
author = "Zhang, {Ke Bing} and Orgun, {Mehmet A.} and Kang Zhang",
year = "2006",
language = "English",
isbn = "3540370250",
volume = "4093 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "316--327",
editor = "Xue Li and Zane, {Osmar R.} and Zhanhuai Li",
booktitle = "Advanced Data Mining and Applications - Second International Conference, ADMA 2006, Proceedings",
address = "United States",
note = "2nd International Conference on Advanced Data Mining and Applications, ADMA 2006 ; Conference date: 14-08-2006 Through 16-08-2006",
}