Cluster based data reduction method for transaction datasets

Autor:	Mohammed Alweshah, Wael Ahmad AlZoubi, Abdulsalam Alarabeyyat
Rok vydání:	2015
Předmět:	Association rule learning business.industry Computer science Disjoint sets computer.software_genre Machine learning Evolutionary computation Reduction (complexity) Feature (machine learning) Data mining Artificial intelligence business Cluster analysis computer Database transaction Data reduction
Zdroj:	2015 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE).
DOI:	10.1109/iscaie.2015.7298332
Popis:	The common feature of transaction datasets is that it is very huge in size, so it is important to develop a technique for dataset reduction. The process of dataset reduction must not change the features of the original dataset; this will increase the effectiveness and efficiency of extracting association rules from these datasets without affecting the original data. Disjoint clusters that have different number of transactions will be introduced in order to minimize the search space, this in turn will decrease the time required to mine the desired rules by dealing with each cluster individually. The support and confidence measures will be used to determine the frequent item sets and exclude the others.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0d1b5c5f830b599ad3b8cc9d1c7b2c58 https://doi.org/10.1109/iscaie.2015.7298332 Zobrazit plný text záznamu