Properties of a Granular Computing Framework for Mining Relational Data

被引：4
|
作者：
Honko, Piotr [1 ]
机构：
[1] Bialystok Tech Univ, Fac Comp Sci, PL-15351 Bialystok, Poland
来源：
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS | 2017年 / 32卷 / 03期
关键词：
D O I：
10.1002/int.21839
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This work investigates properties of a framework for mining relational data. The framework is constructed based on granular computing theory and is equipped with a method for deriving information granules from relational data. Such granules are the basis for discovering knowledge of a different type. It is shown in the paper that thanks to the properties one can improve the performance of tasks such as relational objects representation, search space limitation, and relational patterns generation. (C) 2016 Wiley Periodicals, Inc.
引用
收藏
页码：227 / 248
页数：22
相关论文
共 50 条
  • [41] A Decomposition Framework for Computing and Querying Multidimensional OLAP Data Cubes over Probabilistic Relational Data
    Cuzzocrea, Alfredo
    Gunopulos, Dimitrios
    FUNDAMENTA INFORMATICAE, 2014, 132 (02) : 239 - 266
  • [42] On multi-relational data mining for foundation of data mining
    Liu, Miao
    Guo, Hai-Feng
    Chen, Zhengxin
    2007 IEEE/ACS INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS, VOLS 1 AND 2, 2007, : 389 - +
  • [43] The Review of Outlier Mining Based on Granular Computing
    Liu, Shuang
    Wang, Jiyi
    Xing, Guolin
    2008 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2, 2008, : 462 - 465
  • [44] Hybrid Data Mining Algorithm in Cloud Computing using MapReduce Framework
    Sahay, Siddharth
    Khetarpal, Suruchi
    Pradhan, Tribikram
    PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES (ICACCCT), 2016, : 507 - 511
  • [45] IEEE ACCESS SPECIAL SECTION EDITORIAL: DATA MINING AND GRANULAR COMPUTING IN BIG DATA AND KNOWLEDGE PROCESSING
    Ding, Weiping
    Yen, Gary G.
    Beliakov, Gleb
    Triguero, Isaac
    Pratama, Mahardhika
    Zhang, Xiangliang
    Li, Hongjun
    IEEE ACCESS, 2019, 7 : 47682 - 47686
  • [46] Mining@home: toward a public-resource computing framework for distributed data mining
    Lucchese, C.
    Mastroianni, C.
    Orlando, S.
    Talia, D.
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2010, 22 (05): : 658 - 682
  • [47] Data mining for fuzzy relational data servers
    Velmisov, A. P.
    Stetsko, A. A.
    Yarushkina, N. G.
    THEORETICAL ADVANCES AND APPLICATIONS OF FUZZY LOGIC AND SOFT COMPUTING, 2007, 42 : 220 - +
  • [48] Granular Computing Approach to Finding Association Rules in Relational Database
    Qiu, Taorong
    Chen, Xiaoqing
    Liu, Qing
    Huang, Houkuan
    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2010, 25 (02) : 165 - 179
  • [49] Data science, big data and granular mining
    Pal, Sankar K.
    Meher, Saroj K.
    Skowron, Andrzej
    PATTERN RECOGNITION LETTERS, 2015, 67 : 109 - 112
  • [50] A Wordification Approach to Relational Data Mining
    Perovsek, Matic
    Vavpetic, Anze
    Cestnik, Bojan
    Lavra, Nada
    DISCOVERY SCIENCE, 2013, 8140 : 141 - 154
12345