High utility itemset mining using dolphin echolocation optimization
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ORIGINAL RESEARCH
High utility itemset mining using dolphin echolocation optimization N. Pazhaniraja1 · S. Sountharrajan1 Received: 25 March 2020 / Accepted: 22 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The critical issue identified in recent times is the high utility itemset mining (HUIM). It may be used for showing products that are profitably utilizing considering the factors of profit and quality as opposed to the frequent itemset (FIM) or the association rule (ARM) mining. There are numerous high utility itemset mining (HUIs) algorithms, mostly designed for handling the exponential search space to discover the HUIs at the time the number of the distinct items and the database that was very large. For this purpose, a meta-heuristic algorithm is designed for HUIs mining, working based on the genetic algorithm (GA) and the Dolphin echolocation optimization (DEO). The intended purpose of this evolutionary computation (EC) techniques on the DEO, here only fewer parameters are required to be compared to the approaches that are based on the GA. As the traditional DEO mechanism had been found for handling this continuous problem, an efficient algorithm based on the DEO called the high-utility itemset mining-DEO (HUIM-DEO) was proposed. To prove that the algorithm proposed was able to outperform the other heuristic algorithms to mine the HUIs for the time taken for execution, the number of HUIs discovered, and their convergence. Keywords Data mining · Genetic algorithm (GA) · High utility itemset mining (HUIM) · Dolphin echolocation optimization (DEO) algorithm · High utility itemsets (HUIs)
1 Introduction Data mining discover the predictive information that is hidden among the huge datasets, which is one of the most crucial information available in the data warehouses (Deepa and Vaishnavi 2018). Data mining is called knowledge discovery in database (KDD), which states the discovery of mining of knowledge from huge volumes of data. These practices are applied for operating huge data sizes for discovering the hidden relationships or patterns that are useful in decision making. Thus, there are many types of research involving the term “knowledge discovery in data” in data mining. This KDD process includes data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge representation. The primary goal in knowledge discovery and data mining was to * S. Sountharrajan [email protected] N. Pazhaniraja [email protected] 1
Department of Computing Science and Engineering, VIT Bhopal University, Sehore, MP, India
identify unknown patterns among large datasets to interpret information and knowledge that is useful. Frequent itemset mining (FIM) can probably be an essential part of data analysis and data mining. This FIM attempts different types of information extraction obtained from all databases, which may be an event and sometimes another set of events occurring frequently based on the threshold of minimum f
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