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The ability to scientifically forecast the price of aquatic products plays an important role in the healthy and sustainable development of aquaculture. This paper presents a method for forecasting aquatic product prices using Case-Based Reasoning. Some key processes include automatic extraction of web data, attribute-oriented induction based on concept hierarchy, generation and representation of cases, cases matching and similarity calculation, case evaluation and revision. An application has been implemented using these processes. Experiments have shown that the system can automatically extract web data from multiple websites with information on aquatic product prices and can effectively analyze and forecast prices accordingly.

A CBR Based Prediction Method for Web Aquatic Products Prices