Earticle

초록

영어

This paper presents the applications of dependency of attributes in information systems for data mining from business datasets. Firstly, we present the theoretical framework for data clustering on small business dataset. It is based on a construction of a hierarchical rough set approximation in an information system for data splitting. The hierarchy is defined by the notion of a nested sequence of indiscernibility relations that can be defined from the dependency of attributes. Secondly, an application of such hierarchy for mining maximal association from a business transactional data is presented. It is shown that the dependency provides clear and provable theoretical approach for data clustering and maximal association rules mining.

목차

Abstract
 1. Introduction
 2. Rough Set Theory
  2.1. Information System
  2.2. Indiscernibility Relation
  2.3. Set Approximations
  2.4. Dependency of Attributes
 3. A Construction of Hierarchical Rough Set Approximation in Information Systems using Dependency of Attributes
  3.1. Nested Rough Set Approximations
  3.2. Hierarchical Rough Set Approximations
 4. Applications in Business Data Mining
  4.1. Data Clustering
  4.2. Maximal Association Rules Mining
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Rokhmat Rokhmat [ Faculty of Information Technology and Business Universitas Teknologi Yogyakarta Kampus UTY, Jalan Lingkar Utara, Yogyakarta, Indonesia ]
• Yunus Indra Purnama [ Faculty of Information Technology and Business Universitas Teknologi Yogyakarta Kampus UTY, Jalan Lingkar Utara, Yogyakarta, Indonesia ]
• Eka Novita Sari [ AMCS Research Center Yogyakarta, Indonesia ]
• Tutut Herawan [ AMCS Research Center Yogyakarta, Indonesia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605