Parallel mining of frequent closed patterns: Harnessing modern computer architectures

被引:0
|
作者
Lucchese, Claudio [1 ]
Orlando, Salvatore [1 ]
Perego, Raffaele [2 ]
机构
[1] Ca Foscari Univ, Venice, Italy
[2] CNR, Pisa, Italy
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Inspired by emerging multi-core computer architectures, in this paper we present MT_CLOSED, a multi-threaded algorithm for frequent closed itemset mining (FCIM). To the best of our knowledge, this is the first FCIM parallel algorithm proposed so far We studied how different duplicate checking techniques, typical of FCIM algorithms, may affect this parallelization. We showed that only one of them allows to decompose the global FCIM problem into independent tasks that can be executed in any order and thus in parallel. Finally we show how MT_CLOSED efficiently harness modem CPUs. We designed and tested several parallelization paradigms by investigating static/dynamic decomposition and scheduling of tasks, thus showing its scalability w.r.t. to the number of CPUs. We analyzed the cache friendliness of the algorithm. Finally, we provided additional speed-up by introducing SIMD extensions.
引用
收藏
页码:242 / +
页数:2
相关论文
共 50 条
  • [1] An Efficient Parallel Method for Mining Frequent Closed Sequential Patterns
    Bao Huynh
    Bay Vo
    Snasel, Vaclav
    [J]. IEEE ACCESS, 2017, 5 : 17392 - 17402
  • [2] PrefixFPM: a parallel framework for general-purpose mining of frequent and closed patterns
    Da Yan
    Wenwen Qu
    Guimu Guo
    Xiaoling Wang
    Yang Zhou
    [J]. The VLDB Journal, 2022, 31 : 253 - 286
  • [3] PrefixFPM: a parallel framework for general-purpose mining of frequent and closed patterns
    Yan, Da
    Qu, Wenwen
    Guo, Guimu
    Wang, Xiaoling
    Zhou, Yang
    [J]. VLDB JOURNAL, 2022, 31 (02): : 253 - 286
  • [4] Parallel algorithm for mining frequent closed sequences
    Ma, CX
    Li, QH
    [J]. AUTONOMOUS INTELLIGENT SYSTEMS: AGENTS AND DATA MINING, PROCEEDINGS, 2005, 3505 : 184 - 192
  • [5] On the design of hardware architectures for parallel frequent itemsets mining
    Letras, Martin
    Bustio-Martinez, Lazaro
    Cumplido, Rene
    Hernandez-Leon, Raudel
    Feregrino-Uribe, Claudia
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2020, 157 (157)
  • [6] Mining frequent closed patterns in microarray data
    Cong, G
    Tan, KL
    Tung, AKH
    Pan, F
    [J]. FOURTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, 2004, : 363 - 366
  • [7] Mining frequent closed patterns in pointset databases
    Lee, Anthony J. T.
    Tsao, Wen-Kwang
    Chen, Po-Yin
    Lin, Ming-Chih
    Yang, Shih-Hui
    [J]. INFORMATION SYSTEMS, 2010, 35 (03) : 335 - 351
  • [8] Mining frequent closed patterns by adaptive pruning
    Liu, Jun-Qiang
    Sun, Xiao-Ying
    Zhuang, Yue-Ting
    Pan, Yun-He
    [J]. Ruan Jian Xue Bao/Journal of Software, 2004, 15 (01): : 94 - 102
  • [9] An incremental algorithm for mining frequent closed patterns
    Shi, Huai-Dong
    Cai, Ming
    Wu, Hong-Sen
    Dong, Jin-Xiang
    Fu, Hao
    [J]. Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science), 2009, 43 (08): : 1389 - 1395
  • [10] Parallel algorithm for mining maximal frequent patterns
    Wang, H
    Xiao, ZT
    Zhang, HJ
    Jiang, SY
    [J]. ADVANCED PARALLEL PROCESSING TECHNOLOGIES, PROCEEDINGS, 2003, 2834 : 241 - 248