basically, medical diagnosis problems are the most effective component of treatment policies. recently, significant advances have been formed in medical diagnosis fields using data mining techniques. data mining or knowledge discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. in this paper, bayesian classifier is used as a non-linear dat...

abstract: although all university majors are prominent and the necessity of their presences is of no question, they might not have the same priority basis considering different resources and strategies that could be spotted for a country. this paper focuses on clustering and ranking university majors in iran. to do so, a model is presented to clarify the procedure. eight different criteria are ...

Process mining uses event logs to learn and reason about business process models. Existing algorithms for mining the control-flow of processes in general do not take into account the probabilistic nature of the underlying process, which affects the behaviour of algorithms and the amount of data needed for confidence in mining. We contribute a first step towards a novel probabilistic framework w...

The chapter will describe a new integrated approach to data mining by seamlessly combining business intelligence software functions with manual graphical analysis capabilities with automated data mining techniques. The integrated approach provides an analytic system that supports the KDD process in an integrated manner. Furthermore it recognizes the importance of manual analysis as an important...

Process mining is the extraction of a process model from system logs. These logs have to meet minimum requirements, i.e. each event should refer to a case and a task. Many system logs do not meet these requirements, and therefore it is not possible to use process mining for process optimization or delta analysis. This paper shows an alternative process mining procedure for logs containing data ...

Automated process discovery represents the defining capability of process mining. By exploiting transactional data from information systems, it aims to extract valuable process knowledge. Through process mining, an important link between two disciplines – data mining and business process management – has been established. However, while methods of both data mining and process management are wel...

High utility itemset mining (HUIM) is a new emerging field in data mining which has gained growing interest due to its various applications. The goal of this problem is to discover all itemsets whose utility exceeds minimum threshold. The basic HUIM problem does not consider length of itemsets in its utility measurement and utility values tend to become higher for itemsets containing more items...