Single- and multi-order Neurons for recursive unsupervised learning

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, t...

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Main Authors: RAMANATHAN, Kiruthika, GUAN, Sheng Uei
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Language:English
Published: Institutional Knowledge at Singapore Management University 2008
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Online Access:https://ink.library.smu.edu.sg/sis_research/7432
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spelling sg-smu-ink.sis_research-84352022-10-13T03:42:02Z Single- and multi-order Neurons for recursive unsupervised learning RAMANATHAN, Kiruthika GUAN, Sheng Uei 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. 2008-01-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/7432 info:doi/10.4018/978-1-59904-705-8.ch008 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
RAMANATHAN, Kiruthika
GUAN, Sheng Uei
Single- and multi-order Neurons for recursive unsupervised learning
description 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.
format text
author RAMANATHAN, Kiruthika
GUAN, Sheng Uei
author_facet RAMANATHAN, Kiruthika
GUAN, Sheng Uei
author_sort RAMANATHAN, Kiruthika
title Single- and multi-order Neurons for recursive unsupervised learning
title_short Single- and multi-order Neurons for recursive unsupervised learning
title_full Single- and multi-order Neurons for recursive unsupervised learning
title_fullStr Single- and multi-order Neurons for recursive unsupervised learning
title_full_unstemmed Single- and multi-order Neurons for recursive unsupervised learning
title_sort single- and multi-order neurons for recursive unsupervised learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2008
url https://ink.library.smu.edu.sg/sis_research/7432
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