Sequential Hierarchical Pattern Clustering

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


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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Event Dates: September 2009
Venue - Dates: Pattern Recognition in Bioinformatics, 2009-09-01
Organisations: Southampton Wireless Group
ePrint ID: 268199
Date :
Date Event
September 2009Published
Date Deposited: 11 Nov 2009 19:36
Last Modified: 17 Apr 2017 18:38
Further Information:Google Scholar

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