IdeaGraph: A Graph-Based Algorithm of Mining Latent Information for Human Cognition

• Published in: 2013 IEEE International Conference on Systems, Man, and Cybernetics pp. 952 - 957
• Main Authors: Hao Wang, Fanjiang Xu, Xiaohui Hu, Ohsawa, Yukio
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.10.2013
• Subjects: Algorithm design and analysis; Chance Discovery; Clustering algorithms; Cognition; Data mining; Decision making; IdeaGraph; KeyGraph; Knowledge discovery; Latent Information; Technological innovation
• ISSN: 1062-922X
• DOI: 10.1109/SMC.2013.167

Knowledge discovery in texts (KDT) has been widely applied for business data analysis, but it only reveals a common pattern based on large amounts of data. Since 2000, chance discovery (CD) as an extension of KDT has been proposed to detect rare but significant events or situations regarded as chance candidates for human decision making. Key Graph is a useful and important algorithm as well as a tool in CD for mining and visualizing these chances. However, a scenario graph visualized by Key Graph is machine-oriented, causing a bottleneck of human cognition. Traditional knowledge discovery also runs into the similar problem. In this paper, we propose a human-oriented algorithm called IdeaGraph which can generate a rich scenario graph for human's perception, comprehension and even innovation. IdeaGraph not only works on discovering more rare and significant chances, but also focuses on uncovering latent relationships among chances for gaining richer and deeper human insights. Our experiment has validated the advantages of IdeaGraph by comparing with Key Graph.