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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.
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