Single- and Multi-order Neurons for recursive unsupervised learning

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Author(s): Kiruthika Ramanathan (National University of Singapore, Singapore)and Sheng Uei Guan (Brunel University, UK)
Copyright: 2008
Pages: 17
Source title: Artificial Intelligence for Advanced Problem Solving Techniques
Source Author(s)/Editor(s): Ioannis Vlahavas (Aristotle University, Greece)and Dimitris Vrakas (Aristotle University, Greece)
DOI: 10.4018/978-1-59904-705-8.ch008

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

In this chapter we present a recursive approach to unsupervised learning. The algorithm proposed, while similar to ensemble clustering, does not need to execute several clustering algorithms and find consensus between them. On the contrary, grouping is done between two subsets of data at one time, thereby saving training time. Also, only two kinds of clustering algorithms are used in creating the recursive clustering ensemble, as opposed to the multitude of clusterers required by ensemble clusterers. In this chapter a recursive clusterer is proposed for both single and multi order neural networks. Empirical results show as much as 50% improvement in clustering accuracy when compared to benchmark clustering algorithms.

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Single- and Multi-order Neurons for recursive unsupervised learning

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