Detail publikace

Mining Association Rules from Relational Data - Average Distance Based Method

BARTÍK, V. ZENDULKA, J.

Originální název

Mining Association Rules from Relational Data - Average Distance Based Method

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

The paper describes a new method for association rule discovery in relational databases, which contain both quantitative and categorical attributes. Most of the methods developed in the past are based on initial equi-depth discretization of quantitative attributes. These approaches bring the loss of information. Distance-based methods are another kind of methods. They try to respect the semantics of data. The basic idea of the new method is to separate processing of categorical and quantitative attributes. The first step finds frequent itemsets containing only values of categorical attributes and then quantitative attributes are processed one by one. Discretization of values during quantitative attributes processing is distance-based. A new measure called average distance is introduced for these purposes. The paper describes the method and results of several experiments on real world data.

Klíčová slova

association rule, frequent itemset, categorical attribute, quantitative attribute

Autoři

BARTÍK, V.; ZENDULKA, J.

Rok RIV

2003

Vydáno

1. 11. 2003

ISSN

0302-9743

Periodikum

Lecture Notes in Computer Science

Ročník

2003

Číslo

2888

Stát

Spolková republika Německo

Strany od

757

Strany do

766

Strany počet

10

BibTex

@article{BUT41989,
  author="Vladimír {Bartík} and Jaroslav {Zendulka}",
  title="Mining Association Rules from Relational Data - Average Distance Based Method",
  journal="Lecture Notes in Computer Science",
  year="2003",
  volume="2003",
  number="2888",
  pages="757--766",
  issn="0302-9743"
}