TKFIM: Top-K frequent itemset mining technique based on equivalence classes

doi:10.7717/peerj-cs.385

	TKFIM: Top-K frequent itemset mining technique based on equivalence classes
	Iqbal, Saood 1; Shahid, Abdul 1; Roman, Muhammad 1; Khan, Zahid 2; Al-Otaibi, Shaha 3; Yu, Lisu 4,5
刊名	PEERJ COMPUTER SCIENCE
	2021-03-08
页码	27
关键词	Frequent Itemsets Support Threshold Algorithm Analysis Top-k Frequent Itemsets Artifical Intelligence
ISSN号	2376-5992
DOI	10.7717/peerj-cs.385
英文摘要	Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset's characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.
资助项目	State Key Laboratory of Computer Architecture (ICT, CAS)[CARCHB202019] ; Nanchang University, Nanchang, Jiangxi, PR of China ; Deanship of Scientific Research at Princess Nourah bint Abdulrahman University
WOS研究方向	Computer Science
语种	英语
出版者	PEERJ INC
WOS记录号	WOS:000626442700001
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/16845]
专题	中国科学院计算技术研究所
通讯作者	Yu, Lisu
作者单位	1.Kohat Univ Sci & Technol, Inst Comp, Kohat, Kpk, Pakistan 2.Prince Sultan Univ, Robot & Internet Things Lab, Riyadh, Saudi Arabia 3.Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Informat Syst Dept, Riyadh, Saudi Arabia 4.Nanchang Univ, Sch Informat Engn, Nanchang, Jiangxi, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Iqbal, Saood,Shahid, Abdul,Roman, Muhammad,et al. TKFIM: Top-K frequent itemset mining technique based on equivalence classes[J]. PEERJ COMPUTER SCIENCE,2021:27.
APA	Iqbal, Saood,Shahid, Abdul,Roman, Muhammad,Khan, Zahid,Al-Otaibi, Shaha,&Yu, Lisu.(2021).TKFIM: Top-K frequent itemset mining technique based on equivalence classes.PEERJ COMPUTER SCIENCE,27.
MLA	Iqbal, Saood,et al."TKFIM: Top-K frequent itemset mining technique based on equivalence classes".PEERJ COMPUTER SCIENCE (2021):27.