Discovery of High Utility Itemsets Using Genetic Algorithm with Ranked Mutation

Abstract

Utility mining is the study of itemset mining from the consideration of utilities. It is the utility-based itemset mining approach to find itemsets conforming to user preferences. Modern research in mining high-utility itemsets (HUI) from the databases faces two major challenges: exponential search space and database-dependent minimum utility threshold. The search space is extremely vast when the number of distinct items and the size of the database are very large. Data analysts must specify suitable minimum utility thresholds for their mining tasks, although they might have no knowledge pertaining to their databases. Moreover, a utility-mining algorithm supports only an itemset with positive item values. To evade these problems, two approaches are presented for mining HUI containing negative item values from transaction databases: with/without specifying the minimum utility threshold through a genetic algorithm with ranked mutation. To the best of our knowledge, this is the first work on mining HUI with negative item values from transaction databases using a genetic algorithm. Experimental results show that approaches described in this article achieve better performance in terms of scalability and efficiency.

High utility-itemset mining and privacy-preserving utility mining

“…Han & Ng, 2007). Since more useful information in high-utility itemsets than in that of the frequent itemsets or sequential patterns, privacy preserving for high-utility itemsets mining (PPUM) is more realistic and critical than PPDM (C. In this section, we propose an efficient PSO-based high-utility itemset mining model, and compared with the state-of-the-art HUPE umu -GRAM algorithm (Kannimuthu & Premalatha, 2014) to evaluate the efficiency of the developed algorithm. Details are described below.…”

“…Two real-world datasets called chess and mushroom (http://fimi.ua.ac.be/data/ (2012)), are used in the experiments. The proposed algorithm was compared with the state-of-the-art evolutionary algorithm HUPE umu -GRAM (Kannimuthu & Premalatha, 2014) in terms of runtime and the number of HUIs. Note that the performed algorithms were all performed for 10,000 iterations and the population size is set as 20.…”

Implementation of Bio-Inspired Algorithms in High Utility Itemset Mining

“…These problems were addressed by bio inspired algorithms. [12] proposes two genetic algorithms(GA) named HUPE umu -GA and HUPE wumu -GA to address the above mentioned problems.HUPE umu -GA is selected when our primary concern is search space and memory usage. Here data analyst inputs minimum utility threshold.Optimal HUI's without specifying minimum utility threshold were generated in HUPE wumu -GA. A swarm intelligence approach called Binary PSO(Particle Swarm Optimization) approach has been suggested by [13] to address the below given problems of Genetic Algorithm.…”

Extraction of High Utility Itemsets using Utility Pattern with Genetic Algorithm from OLTP System

“…In Stage II, the Genetic algorithm [23] is invoked to mine the actual high utility item sets from the PHUI and optimally generate the required items. The genetic algorithm is chosen because it is a promising solution for global search and it is capable of discovering high utility itemsets with corresponding parameters quantity and profit.…”