BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Self-Organizing Maps
On the performance of ant-based clustering
Design and application of hybrid intelligent systems
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In this paper, the authors propose a new approach for topological hierarchical tree clustering inspired from the self-assembly behavior of artificial ants. The method, called SoTree Self-organizing Tree, builds, autonomously and simultaneously, a topological and hierarchical partitioning of data. Each ''cluster'' associated to one cell of a 2D grid is modeled by a tree. The artificial ants similarly build a tree where each ant represents a node/data. The benefit of this approach is the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.