Boolean Based Mining Algorithm for Pattern Discovery Based on Human Interaction

Abstract

Mining is a process that provides useful information on surfing and access pattern information based on capturing the behaviour of the user. Semantic knowledge helps to understand how the users will interact with the system. In this paper, we propose a Boolean based APriori Pattern (APP) algorithm to discover pattern based on human interaction using behavioural analysis. In the process of data mining, we have used a Boolean expression that helps to determine the pattern discovery based on the use of frequent pattern by applying association rules. The behavioural analysis is proposed based on the classification of ideas based on comments concerning positive opinion /contrary opinion during human interaction in the practical scenarios. The behavioural analysis is represented as a tree hierarchy where tree based mining is performed by the tree construction and interaction of flow patterns i.e., frequent patterns. The study shows that the successful pattern can be extracted based on the behavioural analysis of human interaction such as frequent pattern, flow interaction and relationships between the interactions.