A Systematic Literature Review of Utility Itemset Mining Algorithms for Large Datasets
Exponential growth has been measured in the size of data during the last two decades. The mining of
utility itemsets from a large dataset is a challenging issue because of the diverse dimensions of data.
Various itemset mining algorithms have been projected by the researchers to discover relations among
the items of a database. In this paper, a systematic literature review has been presented for different
algorithms, which are being used for utility itemset mining. 37 studies have been selected to answer the
research questions framed for this review based on different methods of mining. These methods have
been sorted into four categories with their benefits, drawbacks, performance, and scalability. It has
been concluded that research efforts should be geared towards more scalable, secure, and safe
methods that can operate more meritoriously on large datasets.
Author's Name: Vandna Dahiya and Sandeep Dalal
Volume: Volume 11
Issues: Volume 11
Keywords: Data Mining, Big Data, Utility Mining, Pattern Mining.