Managerial intuition and the development of executive support systems
Decision Support Systems
Self-Organizing Maps
Data Warehousing, Data Mining, and Olap
Data Warehousing, Data Mining, and Olap
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Using SPSS for Windows: Data Analysis and Graphics
Using SPSS for Windows: Data Analysis and Graphics
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Data mining is finding hidden rules in given dataset using non-traditional methods. The objective is to discover useful or patterns from the given collection of data. This research investigates if the differences in accuracy of “time series forecasting” are related to the differences in one’s cognitive style and subjective emotion. Two kinds of analyses were performed before applying data mining. Firstly, a statistical test was used to see if there was a positive correlation between a number of cognitive styles and subjective emotional states and the accuracy of time-series forecasting. This was not very revealing and the next step are to use a self-organizing neural network (SONN) to see if correlations between these variables could be discovered. The results showed that there were correlations but did not show whether the correlations were positive or negative. Finally data mining was applied to discover which cognitive styles and subjective emotions positively influence forecasting. It was found that subjects who have in analytic style and subjects who have a relaxed mode were more accurate in their judgments than those who do not these characteristics.