SSUP-Growth: A Novel Mining High Utility Algorithm Itemset with Single-Scan of Database

Abstract

High Utility Itemset Mining (HUIM) alludes to the identification of itemsets of high utility in the value-based database UP-Growth algorithm is a standout amongst the best algorithms for overcome the challenge of candidate generation and scan database reputedly of previous algorithms. However, it needs scan database twice to actualize the UP tree. Regarding of the updating existing data with new information, UP-growth needs for twofold scanning of new information and existing information. The fundamental motivation behind this work is to build up another algorithm, Single-Scan Utility Pattern Tree (SSUP-tree), for mining high utility itemsets from transaction database through only single-scan of database. In our algorithm, the details of high-utility itemsets is preserved in a particular data structure of the SSUP-Tree after a single-scan of database. Consequently, it can retrieve the identical UP-tree with a fixed minimum utility threshold. The proposed algorithm required to scan the new data only to update SSUP-tree. In this regard, in order to estimate the execution of the proposed algorithm, the SSUP-tree algorithm has been implemented on synthetic and real datasets. The results of this study revealed that SSUP-tree shows a significant enhancement in the execution in terms of runtime since it keeps the huge databases details in a compact format and it avoids repetition of database scanning.