Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms

• Main Authors: Mukhlash, Imam, A., M. Sidratul Muntaha A. M., Iqbal, Mohammad, Saikhu, Ahmad, Sarno, Riyanarto
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Business process management; Data mining; Data processing; Enterprise resource planning; Internet resources; Pattern analysis; Resource management
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/1.4953990

Rapid technological developments caused the increasing number of computerized data processing. With the increasing complexity of business processes, business process management technologies such as ERP (Enterprise Resource Planning) are increasingly being used. This resulted in the availability of data more abundant so that excavation and search information from the dataset will be a valuable knowledge. In this paper, we have done the process mining to obtain an interesting pattern of event log data. In this research, data mining method that we are used is the sequential pattern mining algorithm using FP-Growth-Prefix Span. In addition, we are also used the fuzzy approach to handle the time interval of the analyzed data, so that the sequential pattern that produced become fuzzy time-interval sequential pattern. The application of these methods in a business processes that produce fuzzy time interval sequential pattern. From the analysis, the result shown that there is a minimum effect on the pattern of the resulting support. Furthermore, the results of the analysis can be used as consideration in the analysis of business processes.