Sequential Hierarchical Pattern Clustering
Sequential Hierarchical Pattern Clustering
Clustering is a widely used unsupervised data analysis technique in machine learning. However, a common requirement amongst many existing clustering methods is that all pairwise distances between patterns must be computed in advance. This makes it computationally expensive and difficult to cope with large scale data used in several applications, such as in bioinformatics. In this paper we propose a novel sequential hierarchical clustering technique that initially builds a hierarchical tree from a small fraction of the entire data, while the remaining data is processed sequentially and the tree adapted constructively. Preliminary results using this approach show that the quality of the clusters obtained does not degrade while reducing the computational needs.
978-3-642-04030-6
79-88
Farran, Bassam
9cee7a24-bb9b-410f-a07d-3d9422f3442d
Ramanan, Amirthalingam
4b287910-5234-42ef-83f0-d9875c319a56
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
September 2009
Farran, Bassam
9cee7a24-bb9b-410f-a07d-3d9422f3442d
Ramanan, Amirthalingam
4b287910-5234-42ef-83f0-d9875c319a56
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Farran, Bassam, Ramanan, Amirthalingam and Niranjan, Mahesan
(2009)
Sequential Hierarchical Pattern Clustering.
Pattern Recognition in Bioinformatics, Sheffield.
.
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Conference or Workshop Item
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Abstract
Clustering is a widely used unsupervised data analysis technique in machine learning. However, a common requirement amongst many existing clustering methods is that all pairwise distances between patterns must be computed in advance. This makes it computationally expensive and difficult to cope with large scale data used in several applications, such as in bioinformatics. In this paper we propose a novel sequential hierarchical clustering technique that initially builds a hierarchical tree from a small fraction of the entire data, while the remaining data is processed sequentially and the tree adapted constructively. Preliminary results using this approach show that the quality of the clusters obtained does not degrade while reducing the computational needs.
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Published date: September 2009
Additional Information:
Event Dates: September 2009
Venue - Dates:
Pattern Recognition in Bioinformatics, Sheffield, 2009-09-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 268199
URI: http://eprints.soton.ac.uk/id/eprint/268199
ISBN: 978-3-642-04030-6
PURE UUID: c87cd79b-ff64-431d-a1a8-23d50305f495
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Date deposited: 11 Nov 2009 19:36
Last modified: 15 Mar 2024 03:29
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Contributors
Author:
Bassam Farran
Author:
Amirthalingam Ramanan
Author:
Mahesan Niranjan
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