Abstract
Grouping data into meaningful clusters is an important task of both artificial intelligence and data mining. An important group of clustering algorithms are density based ones that require calculation of a neighborhood of a given data point. The bottleneck for such algorithms are high dimensional data. In this paper, we propose a new TI-k-Neighborhood-Index algorithm that calculates k-neighborhoods for all points in a given data set by means the triangle inequality. We prove experimentally that the NBC (Neighborhood Based Clustering) clustering algorithm supported by our index outperforms NBC supported by such known spatial indices as VA-file and R-tree both in the case of low and high dimensional data.
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Kryszkiewicz, M., Lasek, P. (2010). A Neighborhood-Based Clustering by Means of the Triangle Inequality. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_35
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DOI: https://doi.org/10.1007/978-3-642-15381-5_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15380-8
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