DISCOVERING OF PATTERNS TO GATHER NEW KNOWLEDGE USING COMPLETE RANDOM TREE

Computer Science and Engineering

Discovering new knowledge is significant for understanding and interpreting how people interact in regular problems. In this work, we propose a mining method to extract frequent patterns of problem solving based on the captured contentof face-to-face meetings. Problem solving techniques, such as proposing an idea, giving comments, and expressing a positive opinion, indicate user intention toward a topic or role in a discussion. Problem solving flow in a discussion session is represented as a tree. Tree based interaction mining algorithms are designed to analyze the structures of the trees and to extract interaction flow patterns. Theexperimental results show that we can successfully extract several interesting patterns that are useful for the interpretation of humanbehavior in meeting discussions, such as determining frequent interactions, typical interaction flows, and relationships betweendifferent types of interactions.

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