Finding scientific experts of ACM categories based on temporal association rule mining

Data mining is the process of analyzing the data from different perception and abridgment it into useful information. Association rule mining dig out some valuable relationships among different items in the dataset. Temporal association rule is introduced to find the valuable relationship between the different item set. In scientific literature the data is growing dramatically. Finding experts in academics as well as in enterprises is an important practical problem. Both automated and manually approaches are developed for finding the experts. Manual approaches need extensive human efforts. Mostly developed Automated approaches consider the only one factor and also not cattier the temporal information. Incorporating temporal information for finding the experts in the domain of science and technology is most serious aspect. To solve these problems we proposed the Temporal Expertise mining (TEM) algorithm. TEM algorithm is solving the existing problems. By using TEM algorithm we find the categories based experts. TEM algorithm carter the different factors to solve the problem. These facts are author publications, author category publications and co-author network. The publications are considered in the particular time period. Working of the proposed algorithm is elaborated with case study. Experimental results show the significance of the proposed algorithm.