A knowledge discovery method: APL implementation and application

ABSTRACT

The paper concentrates on one direction of Knowledge Discovery in Data Bases and Data Mining, known as automatic rule extraction from data sets.The rule extraction algorithm has been implemented in Dyalog APL. The paper presents APL functions and tests made on artificial data sets.The real life problem solution is described as an application of implemented APL code. The problem is to find automatically rules to describe the corrosion rate of steel in sodium as a function of alloy additions. The input data are experimental data of corrosion rate measured for different steel samples. The output is a set of IF-THEN rules, which describe the dependence of corrosion rate on alloy additions. An expert in corrosion has validated the rules and an example of real "discovery" has been mentioned.

REFERENCES

• 1: Data mining. Special issue. Communication on ACM 39, 11, (entire issue, November 1996).
• 2: Meta Group (1997), Data mining market trends: A multiclient study. http://www3.shore.net/~kht/workshop/papers.htm
• 3: Nevzorov, B. A. et.al. Materials Corrosion in Sodium. Atomizdat, Moscow, 1977.
• 4: Zagoruiko, N. G. Applied Methods of Data and Knowledge Analysis. Novosibirsk, published by Institute of Mathematics, 1999.

INDEX TERMS

Primary Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.2 Language Classifications

          Nouns: APL

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.4 Knowledge Representation Formalisms and Methods

General Terms:
Algorithms, Experimentation, Languages