Design of a Locationbased Word Recommendation System Based on Association Rule Mining Analysis Association rule mining is the process of discovering new knowledge and nontrivial patterns hidden in textual datasets. This approach is often used by learning analytics researchers and practitioners for exploring unique types of data collected from various educational environments. By leveraging association rule mining, this study aims at providing a solution to a critical challenge that most of the locationbased informal learning tools face namely, the limited scopes to learn new words. Hence, the purpose of this work is to design a word recommendation system that can analyze a learneru0027s previously learned words in order to suggest new vocabulary that can be acquired in a particular learning location. We designed a study and explored a dataset for uncovering the insight of language learnersu0027 learning behaviors. 20 EFL (English as Foreign Language) learnersu0027 data, whose learning location was Tokushima region of Japan, is analyzed by using association rule mining technique. The result indicates thatFirst, in informal learning context, EFL learnersu0027 word learning interests vary much from each other although the learning location is same; and Second, a word recommendation system can be introduced by using association rule mining analysis; however, the possibility of getting highly associated words are low compared with random words.

Development of RuleBased Agents for Autonomous Parking Systems by Association Rules Mining Association Rules Mining is an approach to discover rules from data sets, and it can establish relationships among elements in a data set. Our research is focused on rulebased agents with Artificial General Intelligence (AGI), which are developed based on the overall environment to achieve functions with cognition. In this paper, we use a modified Association Rules Mining method to find out characteristic rules from data recorded in the training of customized parking scenarios. Fuzzy symbolic elements are recorded during training, and Association Rule Mining selects rules for the AI agent. Experiments have been conducted in a virtual environment to demonstrate the effectiveness of the proposed new algorithm.