Mining Frequents Itemset and Association Rules in Diabetic Dataset

Data mining is a field of science to extract and analyses the information from large dataset. One of the most techniques is association rule mining. It aim is to find the relationship between the different attributes of data. Several algorithms for extracting data have been developed. Among the exis...

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Bibliographic Details
Published inBusiness Intelligence Vol. 449; pp. 146 - 157
Main Authors Fakir, Youssef, Maarouf, Abdelfatah, El Ayachi, Rachid
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2022
Springer International Publishing
SeriesLecture Notes in Business Information Processing
Subjects
Association rules
CFP-Growth
Data mining
FP-Growth
Frequent patterns
ICFP-Growth
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Summary:Data mining is a field of science to extract and analyses the information from large dataset. One of the most techniques is association rule mining. It aim is to find the relationship between the different attributes of data. Several algorithms for extracting data have been developed. Among the existing algorithms the FP-Growth algorithm is one of well-know algorithm in finding out the desired association rules. The aim of this paper is the extraction of association rules by FP-Growth algorithm and its variants using a diabetic dataset, which are the CFP-Growth and ICFP-Growth. Experimental results show that the ICFP-Growth is more accurate than CFP-Growth and FP-Growth.
ISBN:9783031064579
3031064577
ISSN:1865-1348
1865-1356
DOI:10.1007/978-3-031-06458-6_12