A new framework for mining frequent interaction patterns from meeting databases. (October 2015)

• Record Type: Journal Article
• Authors: Fariha, Anna; Ahmed, Chowdhury Farhan; Leung, Carson K.; Samiullah, Md.; Pervin, Suraiya; Cao, Longbing
• Is Part Of: Engineering applications of artificial intelligence. Volume 45(2015:Sep.)
• Page Start: 103
• Page End: 118
• Publication Date: 2015-10
• Subjects: Data mining -- Frequent patterns -- Directed acyclic graphs -- Human interaction -- Modelling meetings; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.06.019
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Meetings play an important role in workplace dynamics in modern life since their atomic components represent the interactions among human beings. Semantic knowledge can be acquired by discovering interaction patterns from these meetings. A recent method represents meeting interactions using tree data structure and mines interaction patterns from it. However, such a tree based method may not be able to capture all kinds of triggering relations among interactions and distinguish same interaction from different participants of different ranks. Hence, it is not suitable to find all interaction patterns such as those about correlated interactions. In this paper, we propose a new framework for mining interaction patterns from meetings using an alternative data structure, namely, weighted interaction flow directed acyclic graph ( WIFDAG ). Specifically, a WIFDAG captures both temporal and triggering relations among interactions in meetings. Additionally, to distinguish participants from different ranks, we assign weights to nodes in the WIFDAGs. Moreover, we also propose an algorithm called WDAGMeet for mining weighted frequent interaction patterns from meetings represented by the proposed framework. Extensive experimental results are shown to signify the effectiveness of the proposed framework and the mining algorithm built on that framework for mining frequent interaction patterns from meetings. Abstract : Highlights: We proposed a DAG-based mining framework to model and mine interactions in meetings. The framework integrates DAG, interaction pattern & weighted frequent pattern mining It captures temporal and triggering relations among meeting interactions. It incorporates node weight to preserve rank information of meeting participants. It exploits anti-monotone property and is practical in many real-life scenarios.