ABSTRACT
Frequent pattern mining (FPM) holds a significant position in data mining. Researchers have been a lot more dedicated on this concept from the last couple of years. With its many applications to data mining challenges like clustering and classification, the topic has been widely researched. The advent of FPM into real-world businesses resulted in an increase in profits and also aided in the promotion of sales. FPM has been put into operation in many domains like recommender systems, bioinformatics, and decision making. The literature dedicated to this field of research is abundant and has achieved tremendous progress such as in the development of efficient algorithms for frequent itemset mining. It also has a lot of importance in several requisite data mining tasks as well as in association and correlation analysis, analyzing patterns in spatiotemporal data, cluster analysis, and classification.
Among the main challenges of data mining such as clustering, classification, outlier analysis, and FPM, FPM is considered as a leading problem in data mining by researchers. This chapter will provide a comprehensive study on FPM. The chapter will explore the key algorithms for FPM, privacy concerns, resources available, and different applications of FPM. The chapter concludes by highlighting future advancements.
