Hybrid methods are a branch of biclustering algorithms that emerge from combining selected aspects of pre-existing approaches. The syncretic nature of their construction enriches the existing methods providing them with new properties. In this paper the concept of hybrid biclustering algorithms is explained. A representative hybrid biclustering algorithm, inspired by neural networks and associative artificial intelligence, is introduced and the results of its application to microarray data are presented. Finally, the scope and application potential for hybrid biclustering algorithms is discussed.
CITATION STYLE
Orzechowski, P., & Boryczko, K. (2016). Hybrid biclustering algorithms for data mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9597, pp. 156–168). Springer Verlag. https://doi.org/10.1007/978-3-319-31204-0_11
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