Abstract
We analyze the potentialities of an approach to represent general data records through Boolean vectors in the philosophy of ICA. We envisage these vectors at an intermediate step of a clustering procedure aimed at taking decisions from data. With a “divide et conquer” strategy we first look for a suitable representation of the data and then assign them to clusters. We assume a Boolean coding to be a proper representation of the input of the discrete function computing assignments. We demand the following of this coding: to preserve most information so as to prove appropriate independently of the particular clustering task; to be concise, in order to get understandable assignment rules; and to be sufficiently random, to prime statistical classification methods. In the paper we toss these properties in terms of entropic features and connectionist procedures, whose validation is checked on a series of benchmarks.
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Apolloni, B., Bassis, S., Brega, A. (2008). BICA: A Boolean Indepenedent Component Analysis Approach. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_11
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DOI: https://doi.org/10.1007/978-3-540-87536-9_11
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