ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A pattern mining method for interpretation of interaction
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays
PERCOM '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications
Extraction of important interactions in medical interviewsusing nonverbal information
Proceedings of the 9th international conference on Multimodal interfaces
Mining Impact-Targeted Activity Patterns in Imbalanced Data
IEEE Transactions on Knowledge and Data Engineering
Inferring Human Interactions in Meetings: A Multimodal Approach
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
Smart meeting systems: A survey of state-of-the-art and open issues
ACM Computing Surveys (CSUR)
Discussion ontology: knowledge discovery from human activities in meetings
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
| Hi-index | 0.00 |
In this paper, we propose a mining method to discover high-level semantic knowledge about human social interactions in small group discussion, such as frequent interaction patterns, the role of an individual (e.g., the "centrality" or "power"), subgroup interactions (e.g., two persons often interact with each other), and hot sessions. A smart meeting system is developed for capturing and recognizing social interactions. Interaction network in a discussion session is represented as a graph. Interaction graph mining algorithms are designed to analyze the structure of the networks and extract social interaction patterns. Preliminary results show that we can extract several interesting patterns that are useful for interpretation of human behavior in small group discussion.