Improved Probability Estimator for Human Session Interaction Patterns Using Graph Based Algorithm

International Journal of Computer & Organization Trends  (IJCOT)          
© 2015 by IJCOT Journal
Volume - 5 Issue - 1
Year of Publication : 2015
Authors : P.Naga Revathi Manjusha , S.Sailaja


P.Naga Revathi Manjusha , S.Sailaja "Improved Probability Estimator for Human Session Interaction Patterns Using Graph Based Algorithm", International Journal of Computer & organization Trends (IJCOT), V5(1):6-13 Jan 2015, ISSN:2249-2593, Published by Seventh Sense Research Group.

Abstract—Mining human decision patterns in the meetings or any business interactions are useful to identify the person’s opinion within the session. Activities in the session represent the origins of an individual and mining methods assist to analyze how person delivers their opinion at different paths. In this proposed work, meeting interactions are classified as comment, propose, request-information, acknowledgement, ask, positive and negative opinion. Traditional human interaction techniques are used to detect and analyze patterns to find various types of new knowledge on interactions. Traditional methods fail to extract sub frequent patterns in the interaction flow. Human interaction flow is represented as graph along with their opinions. Graph based pattern mining algorithm was planned to extract relevant patterns from the meeting interaction dataset. Proposed work has extended to extract interaction patterns using DAG (Directed Acyclic Graph) based mining algorithm. Graph-based Substructure mining algorithm which discovers the frequent substructure paths from the candidate patterns of DAG algorithm. Substructures help to predict the probability of associate patterns within the session. Proposed algorithm efficiently uses different support and confident measures to extract user interaction patterns. Experimental study shows that the proposed model extract high relevant interaction patterns with less time and high accuracy.


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Human Meeting, Decision Patterns, Support,Substructure.