A Graph-based Interaction Pattern Discovery for Human Meetings
نویسندگان
چکیده
Mining Human Interaction flow in meetings or general representation of any interaction face to face to meetings is useful to identify the person reaction in dissimilar situation. Activities represent the natural history of the individual and mining methods help to analyze how person delivers their opinion in different ways. Meeting interactions are categorized as propose, comment, acknowledgement, request-information, ask-opinion, positive -opinion and negative opinion. From this Detecting semantic knowledge is significant. Existing system data mining technique to detect and analyze frequent interaction patterns to discover various types of new knowledge on interactions. An interaction flow in user is represented as tree. Tree based pattern mining algorithms was planned to analyze tree structures and extract interaction flow patterns. This work has extend an interaction tree based mining algorithm in two ways: Human interaction flow in a session extraction of the similar events with temporal data mining techniques, it extract the temporal patterns from the captured substance of time series of dissimilar meetings in specific period of time. After that a graph based mining method is proposed to extract the frequent patterns and mining the best meeting pattern. Graph-based Substructure pattern mining which discovers frequent substructures patterns from the face to face meeting not including applicant invention. It builds a new lexicographic order among graphs or tree representation and maps each graph to a unique minimum DFS code as its canonical label with human interaction pattern representation. Based on this lexicographic order adopts the depth-first search approach to extract frequent connected subgraphs proficiently. An experimental result shows that proposed Graph-based Substructure pattern mining algorithm substantially outperforms than the previous tree based mining algorithms.
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