Abstract
The unsupervised nature of many clustering algorithms is one of their key advantages, even though many design decisions must still be made. In discussing some of these design decisions, we will briefly survey advantages and design issues in hierarchical clustering. Similarly, we will review some properties of Adaptive Resonance in engineering applications of clustering. This will lead us to an innovative application of an ART-inspired architecture (BARTMAP) to biclustering, an unsupervised version of heteroassociative learning. Comparison of this approach to other biclustering and traditional clustering approaches illustrates the advantages of biclustering in general and BARTMAP in particular. This advantage is further extended by the development of a new, hierarchical version of BARTMAP.
| Original language | American English |
|---|---|
| Journal | Default journal |
| State | Published - May 1 2012 |
Disciplines
- Electrical and Computer Engineering
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