A Survey on Fast and Scalable Incremental Frequent Item Set Methods for Big Data
Borra Sivaiah, R. Rajeswara Rao · 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) · 2022
The Frequent Pattern Mining (FPM) algorithm, which is commonly used to find frequent itemsets, does not scale well in today's Big Data, especially for dynamically incremental databases. As a result, the FIM algorithms must be developed to support incremental mining. The aim of the Incremental frequent mining is to discover interesting patterns which is used to support data analysis and for making decisions. Incremental mining aims to extract patterns from the dynamic databases which have applications in domains such as product recommendation, text mining, market basket analysis, and web click stream analysis. Several algorithms have been proposed to mine the frequent patterns from both the original and incremental databases. The field of incremental mining is becoming more vibrant and increasing as new algorithms are developed every day. This paper presents a detailed survey of incremental pattern mining, and its methods.