Knowledge Discovery from Relational Databases by Inductive Learning

• Main Authors: Hsueh Ju Fang, 薛如芳
• Other Authors: Hwang Shu Yuen
• Format: Others
• Language: en_US
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/99416981248545720628

碩士 === 國立交通大學 === 資訊工程研究所 === 83 === There is an increasing growing interest in knowledge discovery from/in databases (KDD) research area driven from the rapid increase in the amount of data and databases. Once we can find, discover the "hidden" information among the data, it will be very helpful in many aspects. As a result, there is an increasing demand of tools and techniques for discovering knowledge in databases. There are many KDD methods proposed in recently years. Various approaches including inductive learning system, knowledge acquistion, statistics, information theory ...etc. are applied in this field according to different needs. The motivation of this thesis is to propose a method to discover user-interest rules from different views on databases. The system combines an ILP system and some data extraction methods to discover user-interest descriptions from databases. The discovering process of our system includes four main steps. Initially, users'' queries are transformed into SQL form accepted by SQL server of underlying DBMS system. The second step is to construct training examples possibly embedded with users'' specifications about the views of data from users'' queries reults. The third step is to learn descriptions from the input training examples. Finally, the quality of the learned descriptions should be evaluated and clauses with lower quality are discarded. An example in a relational database has been used as the test database for the primitive KDD system. And results obtained based on users'' intention give a successful implementation for our system.