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Sequential Hierarchical Pattern Clustering

Farran, Bassam, Ramanan, Amirthalingam and Niranjan, Mahesan (2009) Sequential Hierarchical Pattern Clustering At Pattern Recognition in Bioinformatics. , pp. 79-88.

Record type: Conference or Workshop Item (Paper)


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, 2009-09-01
Organisations: Southampton Wireless Group


Local EPrints ID: 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: 18 Jul 2017 06:56

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Author: Bassam Farran
Author: Amirthalingam Ramanan

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