Mining Weighted Frequent Patterns in Incremental Databases

被引:0
|
作者
Ahmed, Chowdhury Farhan [1 ]
Tanbeer, Syed Khairuzzaman [1 ]
Jeong, Byeong-Soo [1 ]
Lee, Young-Koo [1 ]
机构
[1] Kyung Hee Univ, Dept Comp Engn, Youngin Si 446701, Kyunggi Do, South Korea
关键词
Data mining; knowledge discovery; weighted frequent pattern mining; incremental mining; interactive mining;
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
By considering different weights of the items, weighted frequent pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery. However, existing algorithms cannot be applied for incremental and interactive WFP mining because they are based on a static database and require multiple database scans. In this paper, we present a novel tree structure IWFPTWA (Incremental WFP tree based on weight ascending order) and an algorithm IWFPWA for incremental and interactive WFP mining using a single database scan. Extensive performance analyses show that our tree structure and algorithm are efficient for incremental and interactive WFP mining.
引用
收藏
页码:933 / 938
页数:6
相关论文
共 50 条
  • [1] Mining weighted sequential patterns in incremental uncertain databases
    Roy, Kashob Kumar
    Moon, Md Hasibul Haque
    Rahman, Md Mahmudur
    Ahmed, Chowdhury Farhan
    Leung, Carson Kai-Sang
    [J]. INFORMATION SCIENCES, 2022, 582 : 865 - 896
  • [2] Incremental mining of weighted maximal frequent itemsets from dynamic databases
    Yun, Unil
    Lee, Gangin
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2016, 54 : 304 - 327
  • [3] An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
    Yun, Unil
    Shin, Hyeonil
    Ryu, Keun Ho
    Yoon, EunChul
    [J]. KNOWLEDGE-BASED SYSTEMS, 2012, 33 : 53 - 64
  • [4] Single-pass incremental and interactive mining for weighted frequent patterns
    Ahmed, Chowdhury Farhan
    Tanbeer, Syed Khairuzzaman
    Jeong, Byeong-Soo
    Lee, Young-Koo
    Choi, Ho-Jin
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (09) : 7976 - 7994
  • [5] Mining frequent weighted utility patterns with dynamic weighted items from quantitative databases
    Nguyen, Ham
    Le, Nguyen
    Bui, Huong
    Le, Tuong
    [J]. APPLIED INTELLIGENCE, 2023, 53 (16) : 19629 - 19646
  • [6] Mining frequent weighted utility patterns with dynamic weighted items from quantitative databases
    Ham Nguyen
    Nguyen Le
    Huong Bui
    Tuong Le
    [J]. Applied Intelligence, 2023, 53 : 19629 - 19646
  • [7] Weighted Frequent Subgraph Mining in Weighted Graph Databases
    Shinoda, Masaki
    Ozaki, Tomonobu
    Ohkawa, Takenao
    [J]. 2009 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2009), 2009, : 58 - +
  • [8] Mining weighted frequent sequences in uncertain databases
    Rahman, Md Mahmudur
    Ahmed, Chowdhury Farhan
    Leung, Carson Kai-Sang
    [J]. INFORMATION SCIENCES, 2019, 479 : 76 - 100
  • [9] Constructing complete FP-tree for incremental mining of frequent patterns in dynamic databases
    Adnan, Muhaimenul
    Alhajj, Reda
    Barker, Ken
    [J]. ADVANCES IN APPLIED ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2006, 4031 : 363 - 372
  • [10] Efficient Mining of Weighted Frequent Itemsets in Uncertain Databases
    Lin, Jerry Chun-Wei
    Gan, Wensheng
    Fournier-Viger, Philippe
    Hong, Tzung-Pei
    [J]. MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION (MLDM 2016), 2016, 9729 : 236 - 250