Efficiently mining maximal frequent itemsets based on digraph

被引：0

作者：

Ren, Zhibo ^{[1
]}

Zhang, Qiang ^{[2
]}

Ma, Xiujuan ^{[3
]}

机构：

[1] Hebei Univ, Sch Management, Baoding, Peoples R China

[2] Beijing Inst Technol, Sch Management & Econ, Beijing, Peoples R China

[3] North China Elect Power Univ, Baoding, Peoples R China

来源：

FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 2, PROCEEDINGS | 2007年

关键词：

D O I：

10.1109/FSKD.2007.268

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present MFIMiner, a new algorithm for mining maximal frequent itemsets. The algorithm has a preprocessing phase in which a digraph is constructed The digraph represents the set of the frequent 2-itemsets which is the key issue of the performance of the data mining. Then the search for maximal frequent itemsets is done in the digraph. Experiments show that the algorithm is efficient not only to dense data, but to sparse data.

引用

页码：140 / +

页数：2

共 50 条

[21] An efficient approach based on selective partitioning for maximal frequent itemsets mining
Bai, Anita
Dhabu, Meera
Jagtap, Viraj
Deshpande, Parag S.
[J]. SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 2019, 44 (08):
[22] An efficient approach based on selective partitioning for maximal frequent itemsets mining
Anita Bai
Meera Dhabu
Viraj Jagtap
Parag S Deshpande
[J]. Sādhanā, 2019, 44
[23] A New Method for Mining Maximal Frequent Itemsets based on Graph Theory
Nadi, Farzad
Foroozandeh, Atefeh
Hormozi, Shahram Golzari
Shahraki, Mohammad H. Nadimi
[J]. 2014 4TH INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE), 2014, : 183 - 188
[24] Fast mining maximal frequent itemsets; Based on FP-tree
Yan, YJ
Li, ZJ
Chen, HW
[J]. CONCEPTUAL MODELING - ER 2004, PROCEEDINGS, 2004, 3288 : 348 - 361
[25] Mining maximal frequent itemsets from data streams
Mao, Guojun
Wu, Xindong
Zhu, Xingquan
Chen, Gong
Liu, Chunnian
[J]. JOURNAL OF INFORMATION SCIENCE, 2007, 33 (03) : 251 - 262
[26] Parallel mining of maximal frequent itemsets in PC clusters
Veng, Vong Chan
[J]. IMECS 2008: INTERNATIONAL MULTICONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS, VOLS I AND II, 2008, : 480 - 485
[27] MMFI: An effective algorithm for mining maximal frequent itemsets
Ju, Shiguang
Chen, Chen
[J]. 2008 INTERNATIONAL SYMPOSIUM ON INFORMATION PROCESSING AND 2008 INTERNATIONAL PACIFIC WORKSHOP ON WEB MINING AND WEB-BASED APPLICATION, 2008, : 144 - 148
[28] A novel pruning technique for mining maximal frequent itemsets
Ao, Fujiang
Yan, Yuejin
Huang, Jian
Huang, Kedi
[J]. FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 3, PROCEEDINGS, 2007, : 469 - +
[29] Parallel mining of maximal frequent itemsets from databases
Chung, SM
Luo, C
[J]. 15TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2003, : 134 - 139
[30] GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets
Karam Gouda
Mohammed J. Zaki
[J]. Data Mining and Knowledge Discovery, 2005, 11 : 223 - 242

← 1 2 3 4 5 →