Data visualization and data mining of continuous numerical and discrete nominal-valued microarray databases for bioinformatics
Purpose - To present research in the area of the applications of modem heuristics and data mining techniques in knowledge discovery. Design/methodology/approach - Applications of data mining for neural networks using NeuralWare Predict (R) software, genetic algorithms using Biodiscovery GeneSight (R) (2005) software, and regression and discriminant analysis using SPSS (R) were selected for bioscience data sets of continuous numerical-valued Abalone fish data and discrete nominal-valued mushroom data. Findings - This paper illustrates the useful information that can be obtained using data mining for evolutionary algorithms specifically as those for neural networks, genetic algorithms, regression analysis, and discriminant analysis. Research limitations/implications - The use of NeuralWare Predict (R) was a very effective method of implementing training rules for neural networks to identify the important attributes of numerical and nominal valued data. Practical implications - The software and algorithms discussed in the paper can be used to visualize and mine microarray data. Originality/value - The paper contributes to the discussion on the data visualization and data mining of microarray database for bioinformatics and emphasizes new applicability of modem heuristics and software.
databases neural cybernetics programming and algorithm theory data handling