CoCluster: Efficient Mining Maximal Trend Biclusters Without Candidate Maintenance in Discrete Resource Effectiveness Matrix

Zhang, Lihua; Wang, Miao; Gu, Qingfan; Zhai, Zhengjun; Wang, Guoqing

doi:10.1007/978-3-642-54233-6_1

Cited by 2 publications

(1 citation statement)

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“…There are two distinctive research fields on which the research between function and resource relationship absorbed. The first one is the focused on how to mine differential resource patterns from the function's resource effective matrix efficiently [2,3]. The second one gives attention on how to mine biclusters with maximal variant usage rate and maximal low usage rate in function-resource matrix [4,5,6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Mavingire²,

Zan³

et al. 2018

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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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