Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors a temporal database. Most previous studies focused on finding these row (temporal) databases and disregarded the occurrences of columnar databases. Furthermore, naïve approach transforming database into then applying existing algorithms to find interesting not practicable due computational reasons. With this motivation, paper proposes framework discover Our employs novel depth-first search algorithm that compresses given unified dictionary mines it recursively itemsets. The holds information pertaining their Experimental results six demonstrate proposed computationally efficient scalable.