Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map |
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| Affiliation: | 1. Data Science for Knowledge Creation Research Center, Seoul National University, Republic of Korea;2. Department of Industrial Engineering, School of Engineering, Seoul National University, Republic of Korea;3. Department of Statistics, Seoul National University, Republic of Korea;1. Department of Mathematics and Mathematical Statistics, Umeå University, SE-901 87 Umeå, Sweden;2. Department of Computing Science, Umeå University, SE-901 87 Umeå, Sweden;1. Department of Software Engineering, University of Granada, Granada 18071, Spain;2. Department of Marketing and Market Research, Complutense University of Madrid, 28015 Madrid, Spain;3. Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain;4. Department of Electrical and Computer Engineering, King Abdulaziz University, 21589 Jeddah, Saudi Arabia;1. Escuela Superior de Ingenieros, University of Seville, Avda. Camino de los Descubrimientos s/n, 41092 Sevilla, Spain;2. Faculty of Automatic Controls and Computers, University Politehnica of Bucharest, Spl. Independentei 313, Bucharest, Romania;1. Universidad de Cantabria, Av. de los Castros s/n, Santander 39005, Spain;2. Universidad de Oviedo, Calle San Francisco 1, Oviedo 33003, Spain;3. Universidad de Deusto, Av. de las Universidades 24, Bilbao 48007, Spain |
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| Abstract: | Fuzzy cognitive maps (FCMs) are one of the representative techniques in developing scenarios that include future concepts and issues, as well as their causal relationships. The technique, initially dependent on deductive modeling of expert knowledge, suffered from inherent limitations of scope and subjectivity; though this lack has been partially addressed by the recent emergence of inductive modeling, the fact that inductive modeling uses a retrospective, historical data that often misses trend-breaking developments. Addressing this issue, the paper suggests the utilization of futuristic data, a collection of future-oriented opinions extracted from online communities of large participation, in scenario building. Because futuristic data is both large in scope and prospective in nature, we believe a methodology based on this particular data set addresses problems of subjectivity and myopia suffered by the previous modeling techniques. To this end, text mining (TM) and latent semantic analysis (LSA) algorithm are applied to extract scenario concepts from futuristic data in textual documents; and fuzzy association rule mining (FARM) technique is utilized to identify their causal weights based on if-then rules. To illustrate the utility of proposed approach, a case of electric vehicle is conducted. The suggested approach can improve the effectiveness and efficiency of scanning knowledge for scenario development. |
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