Efficient Mining of Maximal Frequent Itemsets Based on M-Step Lookahead

被引：0

作者：

Meyer, Elijah L. ^{[1
]}

Chung, Soon M. ^{[1
]}

机构：

[1] Wright State Univ, Dept Comp Sci & Engn, Dayton, OH 45435 USA

来源：

PROCEEDINGS OF 2018 5TH INTERNATIONAL CONFERENCE ON DATA AND SOFTWARE ENGINEERING (ICODSE) | 2018年

关键词：

frequent itemset mining; maximal frequent itemsets; Max-Miner; performance analysis;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose a new maximal frequent itemset mining algorithm, named m-step lookahead. This is a variant of the Max-Miner algorithm that, instead of counting the support of the largest possible superset of each candidate itemset, counts the support of a superset with a predetermined length. This is designed to circumvent the weakness in the Max-Miner algorithm that the probability of finding a frequent superset is extremely low for the first several passes. By looking for a smaller superset, m-step lookahead may find long frequent patterns more quickly than Max-Miner. Our experimental results demonstrate that this is the case for certain datasets and user-defined parameters.

引用

页数：5

共 50 条

[41] Fast Updating Maximal Frequent Itemsets Based On Full Merged Sorted FP-Tree [J].

Guo Yunkai ;

Yang Junrui ;

Huang Yulei .

2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31, 2008, :11444-11447

[42] A haoop-based parallel mining of frequent itemsets using N-Lists [J].

Sohrabi, Mohammad Karim ;

Taheri, Narjes .

JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS, 2018, 41 (03) :229-238

[43] PrePost+: An efficient N-lists-based algorithm for mining frequent itemsets via Children-Parent Equivalence pruning [J].

Deng, Zhi-Hong ;

Lv, Sheng-Long .

EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (13) :5424-5432

[44] An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering [J].

Sinthuja, M. ;

Pravinthraja, S. ;

Dhanalakshmi, B. K. ;

Gururaj, H. L. ;

Ravi, Vinayakumar ;

Lal, G. Jyothish .

JOURNAL OF SAFETY SCIENCE AND RESILIENCE, 2025, 6 (01) :93-104

[45] A novel process-based association rule approach through maximal frequent itemsets for big data processing [J].

Liu, Zelei ;

Hu, Liang ;

Wu, Chunyi ;

Ding, Yan ;

Wen, Quangang ;

Zhao, Jia .

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 81 :414-424

[46] ParallelCharMax: An Effective Maximal Frequent Itemset Mining Algorithm Based on MapReduce Framework [J].

Gahar, Rania Mkhinini ;

Arfaoui, Olfa ;

Sassi Hidri, Minyar ;

Ben Hadj-Alouane, Nejib .

2017 IEEE/ACS 14TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA), 2017, :571-578

[47] Explore Maximal Frequent Itemsets for Big Data Pre-processing based on Small Sample in Cloud Computing [J].

Xu, Gaochao ;

Ding, Yan ;

Wu, Chunyi ;

Zhai, Yunan ;

Zhao, Jia .

2016 8TH INTERNATIONAL CONGRESS ON ULTRA MODERN TELECOMMUNICATIONS AND CONTROL SYSTEMS AND WORKSHOPS (ICUMT), 2016, :235-239

[48] Comparative Analysis of Genetic Based Approach and Apriori Algorithm for Mining Maximal Frequent Item Sets [J].

Kabir, Mir Md. Jahangir ;

Xu, Shuxiang ;

Kang, Byeong Ho ;

Zhao, Zongyuan .

2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2015, :39-45

[49] Mining Frequent Itemsets Using Proposed Top-Down Approach Based on Linear Prefix Tree (TD-LP-Growth) [J].

Sinthuja, M. ;

Puviarasan, N. ;

Aruna, P. .

INTERNATIONAL CONFERENCE ON COMPUTER NETWORKS AND COMMUNICATION TECHNOLOGIES (ICCNCT 2018), 2019, 15 :23-32

[50] An Efficient Outlier Detection Approach Over Uncertain Data Stream Based on Frequent Itemset Mining [J].

Hao, Shangbo ;

Cai, Saihua ;

Sun, Ruizhi ;

Li, Sicong .

INFORMATION TECHNOLOGY AND CONTROL, 2019, 48 (01) :34-46

← 1 2 3 4 5 →