A step towards the foundations of data mining

被引:10
|
作者
Yao, YY [1 ]
机构
[1] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada
关键词
foundations of data mining; rule mining; formal concepts; explanation oriented mining; data mining models;
D O I
10.1117/12.509161
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper addresses some fundamental issues related to the foundations of data mining. It is argued that there is an urgent need for formal and mathematical modeling of data mining. A formal framework provides a solid basis for a systematic study of many fundamental issues, such as representations and interpretations of primitive notions of data mining, data mining algorithms, explanations and applications of data mining results. A multi-level framework is proposed for modeling data mining based on results from many related fields. Formal concepts are adopted as the primitive notion. A concept is jointly defined as a pair consisting of the intension and the extension of the concept, namely, a formula in a certain language and a subset of the universe. An object satisfies the formula of a concept if the object has the properties as specified by the formula, and the object belongs to the extension of the concept. Rules are used to describe relationships between concepts. A rule is expressed in terms of the intensions of the two concepts and is interpreted in terms of the extensions of the concepts. Several different types of rules are investigated. The usefulness and meaningfulness of discovered knowledge are examined using a utility model and an explanation model.
引用
收藏
页码:254 / 263
页数:10
相关论文
共 50 条
  • [1] TreeFinder: a first step towards XML data mining
    Termier, A
    Rousset, MC
    Sebag, M
    2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, 2002, : 450 - 457
  • [2] TOWARDS A UNIFIED STRATEGY FOR THE PREPROCESSING STEP IN DATA MINING
    Bratu, Camelia Vidrighin
    Potolea, Rodica
    ICEIS 2009 : PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL AIDSS, 2009, : 230 - 235
  • [3] Foundations of predictive data mining
    Jovanovic, N
    Milutinovic, V
    Obradovic, Z
    2002 6TH SEMINAR ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS, 2002, : 53 - 58
  • [4] Privacy Preserving Data Mining Techniques for Hiding Sensitive Data: A Step Towards Open Data
    Toshniwal, Durga
    DATA SCIENCE LANDSCAPE: TOWARDS RESEARCH STANDARDS AND PROTOCOLS, 2018, 38 : 205 - 212
  • [5] Feminist Mining: A Step towards Sustainable Mining in India
    Singh, Priya
    Behura, Ajit Kumar
    PROBLEMY EKOROZWOJU, 2022, 17 (01): : 235 - 245
  • [6] A graduate seminar on foundations of data mining
    Chen, Zhengxin
    2006 IEEE International Conference on Granular Computing, 2006, : 365 - 368
  • [7] Towards personalized recommendation by two-step modified Apriori data mining algorithm
    Lazcorreta, Enrique
    Botella, Federico
    Fernandez-Caballero, Antonio
    EXPERT SYSTEMS WITH APPLICATIONS, 2008, 35 (03) : 1422 - 1429
  • [8] The GUHA method and foundations of (relational) data mining
    Hájek, P
    Holeña, M
    Rauch, J
    THEORY AND APPLICATIONS OF RELATIONAL STRUCTURES AS KNOWLEDGE INSTRUMENTS: COST ACTION 274, TARSKI, REVISED PAPERS, 2003, 2929 : 17 - 37
  • [9] Data Mining through Early Experience Prototyping A step towards Data Driven Product Service System Design
    Ruvald, Ryan
    Frank, Martin
    Johansson, Christian
    Larsson, Tobias
    IFAC PAPERSONLINE, 2018, 51 (11): : 1095 - 1100
  • [10] Special issue: Foundations and advances in data mining - Introduction
    Chu, W
    Lin, TY
    APPLIED INTELLIGENCE, 2005, 22 (01) : 7 - 7