Hybrid Mining Model of Rule Induction Based Adaptive Fuzzy Apriori - ANN Algorithm to Predict the Least Risk Investor Trading in Stock Market

適應性模糊Apriori為基礎之規則歸納混合探勘模式－－於股票市場中使用ANN演算法預測投資者交易的最小風險

本篇文章透過一個概念性架構的說明來評估現代資訊系統對企業業務主要的影響。在發展新的資料探勘技術與軟體來幫助有效率的企業解決方案過程中，我們提出一個「有效的資訊系統」，此系統混合資料探勘工具，包含了Crisp資料庫上的規則歸納，此Crisp資料庫之後被轉換到模糊資料集中，其抽樣理論為基礎的Apriori演算法建立了所有可能的關連規則，最後透過單一層次種子的校準來促進規則為基礎的人工類神經網路提供有建設性的預測與決策。此模式透過混合的方式來提供系統端特徵與適用觀點兩者中的高度智慧系統架構設計之目標。此系統的設計在股票市場投資領域中的顧客端風險管理例子是可行的。

This paper illustrates a conceptual frame work for evaluating the impact of contemporary information systems mainly on business events. In the process of developing new data mining techniques and software to aid in efficient business solutions, we propose an 'efficient information system' that hybrids a tool of data mining incorporating rule induction on crisp database which is then transformed into a fuzzy dataset on which sampling theory based Apriori algorithm creates all possible association rules which are finally calibrated using a single layer feed forward rule based artificial neural network to make suggestive predictions and hence decisions. This model is hybrid to serve the aim of designing system architecture highly intelligent in both system side features and in applicatory aspects. The system design is worked on an example case to deal with the customer side risk management in the area of investment in the stock market.