Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
KeyGraph: Automatic Indexing by Co-occurrence Graph based on Building Construction Metaphor
ADL '98 Proceedings of the Advances in Digital Libraries Conference
Soft risk maps of natural disasters and their applications to decision-making
Information Sciences: an International Journal
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Web intelligence and change discovery
Discovering the rare opportunity by strategy based interactive value-focused thinking model
International Journal of Knowledge-based and Intelligent Engineering Systems - Chance discovery
Information Sciences: an International Journal
Clustering high dimensional data: A graph-based relaxed optimization approach
Information Sciences: an International Journal
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Effective vaccination policies
Information Sciences: an International Journal
Discover the used innovativeness of the early adopters
JSAI-isAI'10 Proceedings of the 2010 international conference on New Frontiers in Artificial Intelligence
Idea discovery: A scenario-based systematic approach for decision making in market innovation
Expert Systems with Applications: An International Journal
Business opportunity: the weak-tie roaming among tribes
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
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There are two directions in data mining research, qualitative analysis and quantitative analysis. Chance Discovery is a useful qualitative analysis method to visualize the data structure and to discover the potential future scenario. But in reality, due to tremendous amount of information, data structure may be too complex for the user to comprehend. In this paper, using Chance Discovery as a basic driving force, we proposed an innovative interactive human-computing process model to extract the data structure of a specific topic that the user is most interested in. Our model combined the strength of both qualitative analysis and quantitative analysis where Grounded theory and text mining technology were applied to sift out meaningful but small data. Experiment results showed that the visualized results generated by our model were more accurate than those obtained by Chance Discovery method. Furthermore, users can evaluate the relevant data structure generated by our model to decide on potential chances.