Abstract
Clustering is one of the most important unsupervised learning problems. It deals with finding a structure in a collection of unlabeled data points. Hierarchical clustering algorithms are typically more effective in detecting the true clustering structure of a structured data set than partitioning algorithms. We find in literature several important research in hierarchical cluster analysis [Jain et al., 1999]. Hierarchical methods can be further divided to agglomerative and divisive algorithms, corresponding to bottom-up and top-down strategies, to build a hierarchical clustering tree. Another works concerning hierarchical classifiers are presented in [Jiang et al., 2010]. In this paper we propose a new way to build a set of self-organized hierarchical trees.
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Azzag, H., Lebbah, M. (2013). A New Way for Hierarchical and Topological Clustering. In: Guillet, F., Pinaud, B., Venturini, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35855-5_5
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DOI: https://doi.org/10.1007/978-3-642-35855-5_5
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