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ANNPR
2008
Springer

Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets

14 years 25 days ago
Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets
Clustering constitutes an ubiquitous problem when dealing with huge data sets for data compression, visualization, or preprocessing. Prototype-based neural methods such as neural gas or the self-organizing map offer an intuitive and fast variant which represents data by means of typical representatives, thereby running in linear time. Recently, an extension of these methods towards relational clustering has been proposed which can handle general non-vectorial data characterized by dissimilarities only, such as alignment or general kernels. This extension, relational neural gas, is directly applicable in important domains such as bioinformatics or text clustering. However, it is quadratic in m both in memory and in time (m being the number of data points). Hence, it is infeasible for huge data sets. In this contribution we introduce an approximate patch version of relational neural gas which relies on the same cost function but it dramatically reduces time and memory requirements. It of...
Alexander Hasenfuss, Barbara Hammer, Fabrice Rossi
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ANNPR
Authors Alexander Hasenfuss, Barbara Hammer, Fabrice Rossi
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