An Extensive Study of Frequent Mining Algorithms for Colossal Patterns

During the last decade of research, a lot of focus has been placed on the subject of frequent pattern mining (FPM). A profitable data set with a large sum of transactions and only a few items in each transaction has been used to develop numerous FPM algorithms. Because of the rise of bioinformatics, a new sort of data set called a high-dimensional data set has emerged, with fewer transactions but a greater sum of elements in each. The execution time of classical algorithms grows with deal length. High-dimensional data sets can’t be processed by existing algorithms. But when applied to large data sets with a lot of dimensions, mining algorithms generate a huge amount of data, much of which is useless to scientists because of the little and medium-sized patterns they include. As a way to lessen the number of output patterns for mining patterns, colossal pattern mining is discussed. Since small and mid-sized patterns aren’t mined, mining algorithms for enormous patterns run faster. In this work, an extensive study of colossal patterns, existing mining algorithms with its drawback is mentioned. The definitions of FPM, high utility mining and relation of colossal patterns with others are also explained. Pattern-Fusion is the first algorithm, which is developed for colossal patterns that is described briefly in this work.