Closed Frequent Itemsets Mining Based on It-Tree
Journal: Journal of Medical Informatics and Decision Making (Vol.1, No. 2)Publication Date: 2020-01-04
Authors : Youssef Fakir; Chaima Ahle Touate; Rachid Elayachi; Mohamed Fakir;
Page : 44-52
Keywords : Data mining; Association rules; frequent closed itemset; CHARM; DCI;
Abstract
In the last decade, the amount of collected data, in various computer science applications, has grown considerably. These large volumes of data need to be analysed in order to extract useful hidden knowledge. This work focuses on association rule extraction. This technique is one of the most popular in data mining. Nevertheless, the number of extracted association rules is often very high, and many of them are redundant. In this paper, we propose an algorithm, for mining closed itemsets, with the construction of an it-tree. This algorithm is compared with the DCI (direct counting & intersect) algorithm based on min support and computing time. CHARM is not memery-efficient. It needs to store all closed itemsets in the memory. The lower min-sup is, the more frequent closed itemsets there are so that the amounts of memory used by CHARM are increasing.
Author Contributions
Other Latest Articles
- Study of The ID3 and C4.5 Learning Algorithms
- The Knowledge and Perception of Hand Hygiene Among Health Care Workers in Clinical Settings in Khartoum State - Sudan
- Measuring Availability and Prices of Locally Produced and Imported Medicines in Sudan
- Distinguish Thyroid Malignant from Benign Alterations using Trace Element Contents in Nodular Tissue determined by Neutron Activation and Inductively Coupled Plasma Mass Spectrometry
- Content of Copper, Iron, Iodine, Rubidium, Strontium and Zinc in Thyroid Malignant Nodules and Thyroid Tissue adjacent to Nodules
Last modified: 2023-03-02 15:02:13