A Graph Based Method to Mine Frequent Dense Resources across Multiple Discrete-Value Function-Resource Effectiveness Matrix

Miao Wang, Talent Paul Mavingire, Xiangzhen Zan, Wenbin Liu, Liangzhong Shen


A function plays a pivotal role when it comes to the improvement of system task information. It is the basis for a guarantee of effectiveness and performance of the system. However, this implies that for the function health to be fully effective its resources need also to be up to par. An antagonistic relationship between the function resource and the function health, wherein the resources are of low standard only brings about downgrade in performance and effectiveness of the function. In this paper, we aim to showcase a study on the relation of functions and resources effectiveness from the data of functions' resource effectiveness matrix and how it can excavate the health relation between resources and functions. In previous studies, most methods focused on designing complex algorithms to extract dense connected resources from one function's resource effectiveness matrix. We propose our own method for the mining of frequent dense resources across multiple discrete-value function-resource effectiveness matrix. It has proven to be better as the experimental results show our algorithm can extract frequent dense resource sets that satisfy the specified conditions in a more efficient way.


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