Generalized Entropy Agglomeration
I\c{s}{\i}k Bar{\i}\c{s} Fidaner
TL;DR
The paper introduces Generalized Entropy Agglomeration (GEA), an extension of the original EA algorithm, capable of handling multiset blocks and numerical data, supported by new software REBUS 2.0.
Contribution
It extends the original EA algorithm to handle more complex data types and introduces a numerical categorization method, with a software implementation REBUS 2.0.
Findings
GEA can process multiset blocks and rational occurrence numbers.
The numerical categorization procedure enables GEA to work with numerical datasets.
REBUS 2.0 software implements these new capabilities.
Abstract
Entropy Agglomeration (EA) is a hierarchical clustering algorithm introduced in 2013. Here, we generalize it to define Generalized Entropy Agglomeration (GEA) that can work with multiset blocks and blocks with rational occurrence numbers. We also introduce a numerical categorization procedure to apply GEA to numerical datasets. The software REBUS 2.0 is published with these capabilities: http://fidaner.wordpress.com/science/rebus2
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Taxonomy
TopicsBayesian Methods and Mixture Models · Statistical Methods and Inference · Advanced Clustering Algorithms Research