Abstract
Data mining has been widely applied for business data analysis, but it only reveals a common pattern based on large amounts of data. In current years, chance discovery as an extension of data mining is built to detect rare but important chances for human decision making. KeyGraph is an algorithm as well as a tool for discovering rare and important events that are regarded as candidates of chances in chance discovery. However, KeyGraph is originally invented as a keyword extraction algorithm, so scenario graph generated from KeyGraph is machine oriented, which causes a bottleneck of human cognition. Traditional data mining methods also have the similar problem. In this paper, we propose a user-oriented algorithm called IdeaGraph which can generate a rich scenario graph for humans’ comprehension, interpretation, and innovation. IdeaGraph not only works on discovering more rare and significant business chances, but also focuses on uncovering latent relationship among them. An experiment indicates the advantages and effects of IdeaGraph by comparing with KeyGraph. IdeaGraph has been integrated in a creativity support system named iChance for collective intelligence.
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Acknowledgments
The author was supported through the Global COE Program “Global Center of Excellence for Mechanical Systems Innovation,” by the Ministry of Education, Culture, Sport, Science, and Technology of Japan and was also funded by Chinese Scholarship Council (CSC).
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Wang, H., Ohsawa, Y., Lv, P., Hu, X., Xu, F. (2014). IdeaGraph: Turning Data into Human Insights for Collective Intelligence. In: Sun, F., Li, T., Li, H. (eds) Foundations and Applications of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37829-4_4
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DOI: https://doi.org/10.1007/978-3-642-37829-4_4
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