期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2013
卷号：4
期号：4
页码：22-26
语种：English
出版社：Ayushmaan Technologies
摘要：Data mining is widely applied in the database research area in order to extract frequent correlations of values from both structured and semi structured datasets. In this work we describe an approach to mine Decision Tree learning association rules from how people interact in a meeting. In this paper, we propose a mining method to extract frequent patterns of human interaction based on the captured content of face-to-face meetings. Human interactions, such as proposing an idea, giving comments, and expressing a positive opinion, indicate user intention toward a topic or role in a discussion. Human interaction flow in a discussion session is represented as a tree. Decision Tree interaction mining algorithms are designed to analyze the structures of the trees and to extract interaction flow patterns. The experimental results show that we can successfully extract several interesting patterns that are useful for the interpretation of human behaviour in meeting discussions, such as determining frequent interactions, typical interaction flows, and relationships between different types of interactions.
关键词：Decision Tree Learning;Human Behaviour;Data Sets;Patterns
