A combination of SEMMA & CRISP-DM models for effectively handling big data using formal concept analysis based knowledge discovery: A data mining approach

Abstract

Data analytics has emerged as one of the most advanced technologies in recent times. However, the successful implementation of analytics is still a great challenge since they suffer from technical barriers and have a lack of structured approaches for performing analytics. Data mining models are considered as a potential tool for solving problems related to data analytics. Data mining is a process used for extracting the relevant attributes from raw data, which is further processed using the mechanism of knowledge discovery for support decision making. Formal concept analysis (FCA) provides a robust platform for knowledge discovery and helps in the successful adoption of data mining for handling big data. Several mining techniques powered by FCA are discussed by the researchers. However, the analysis of FCA suggests that the effectiveness of FCA for big data needs, a deeper investigation in order to expand its application horizon. In this context, this research emphasizes the application of FCA for developing an effective strategy through a combination of SEMMA and CRISP models for handling big data by integrating knowledge discovery with data mining.