Maryam Tavakoli Zaniani; Mohammad Reza Gholamian
Abstract
Process discovery is a branch of process mining that by using event logs extracts the process model that describes the events’ behavior properly. Since, Heuristic process discovery algorithms are among the most significant and popular process discovery methods and due to the fact that the ...
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Process discovery is a branch of process mining that by using event logs extracts the process model that describes the events’ behavior properly. Since, Heuristic process discovery algorithms are among the most significant and popular process discovery methods and due to the fact that the quality of outputs of these algorithms is heavily dependent on the quality of extracted dependency graph, in this paper for the first time, an approach to transform the problem of dependency graph discovery to a binary programming problem has been proposed and also, an objective function is introduced that simultaneously considers fitness and precision measures of output models. The weights dedicated to each of the measures are determined by means of a user-defined threshold. The mentioned measures are the most important metrics in assessing quality of output models of process discovery algorithms. Hence, in fact this approach focuses on improving quality metrics of output models. Moreover, by means of defining suitable constrains, the proposed approach is capable of involving domain knowledge in mining procedure, as well as guiding the result through whether the models that are more likely to be sound. This is depicted in a case study of a real company that is described in this paper. In the case study, the proposed approach has been applied to marketing event log of the mentioned company by utilizing the constrains defined according to domain knowledge and structural rules of dependency graph and at the end, the results were presented.
Mohammad’reza Gholamian; Azimeh Mozafari
Abstract
Management and evaluation of valuable customers, is one of the most important banking factors to reduce costs and increase profitability. In recent decades, many researchers have studied on the analysis of the customer attributes to evaluate value of them using data mining techniques and decision tree ...
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Management and evaluation of valuable customers, is one of the most important banking factors to reduce costs and increase profitability. In recent decades, many researchers have studied on the analysis of the customer attributes to evaluate value of them using data mining techniques and decision tree is one of the most widely used data mining algorithms in the field. Since this algorithm for built tree, considers only one attribute at a time to test each node and ignores the dependency between attributes, therefore, required maximum memory is increased. To solve this problem, in this research a method is proposed to improve the decision tree using neural network to explore the dependency between the attributes based on reduction in required maximum memory that is used based on RFM model to predict customer values. Results show that the proposed method using dependencies between attributes will predict the new customer values by less maximum memory compare to the basic method
