Mining frequent itemsets a perspective from operations research

Many papers on frequent itemsets have been published. Besides somecontests in this field were held. In the majority of the papers the focus ison speed. Ad hoc algorithms and datastructures were introduced. Inthis paper we put most of the algorithms in one framework, usingclassical Operations Research paradigms such as backtracking, depth-first andbreadth-first search, and branch-and-bound. Moreover we presentexperimental results where the different algorithms are implementedunder similar designs.data mining;operation research;Frequent itemsets

A log mining approach for process monitoring in SCADA

SCADA (Supervisory Control and Data Acquisition) systems are used for controlling and monitoring industrial processes. We propose a methodology to systematically identify potential process-related threats in SCADA. Process-related threats take place when an attacker gains user access rights and performs actions, which look legitimate, but which are intended to disrupt the SCADA process. To detect such threats, we propose a semi-automated approach of log processing. We conduct experiments on a real-life water treatment facility. A preliminary case study suggests that our approach is effective in detecting anomalous events that might alter the regular process workflow

Mining frequent itemsets a perspective from operations research

Many papers on frequent itemsets have been published. Besides some contests in this field were held. In the majority of the papers the focus is on speed. Ad hoc algorithms and datastructures were introduced. In this paper we put most of the algorithms in one framework, using classical Operations Research paradigms such as backtracking, depth-first and breadth-first search, and branch-and-bound. Moreover we present experimental results where the different algorithms are implemented under similar designs

Frequent Pattern mining with closeness Considerations: Current State of the art

Due to rising importance in frequent pattern mining in the field of data mining research, tremendous progress has been observed in fields ranging from frequent itemset mining in transaction databases to numerous research frontiers. An elaborative note on current condition in frequent pattern mining and potential research directions is discussed in this article. It2019;s a strong belief that with considerably increasing research in frequent pattern mining in data analysis, it will provide a strong foundation for data mining methodologies and its applications which might prove a milestone in data mining applications in mere future