Detail publikace

Average Distance Based Method For Association Rules Discovery In Relational Databases

Originální název

Average Distance Based Method For Association Rules Discovery In Relational Databases

Anglický název

Average Distance Based Method For Association Rules Discovery In Relational Databases

Jazyk

en

Originální abstrakt

The paper describes the new method for association rule discovery in relational databases, which contain quantitative and categorical attributes. Many methods have been developed yet. But most of these methods are based on initial equi-depth discretization of quantitative attributes. These approaches bring the loss of information. The basic idea of the new method is to separate processing of categorical and quantitative attributes. The first step finds frequent k-itemsets containing only values of categorical attributes and then quantitative attributes are processed one by one. Discretization of values during quantitative attributes processing is based on a new measure called average distance.

Anglický abstrakt

The paper describes the new method for association rule discovery in relational databases, which contain quantitative and categorical attributes. Many methods have been developed yet. But most of these methods are based on initial equi-depth discretization of quantitative attributes. These approaches bring the loss of information. The basic idea of the new method is to separate processing of categorical and quantitative attributes. The first step finds frequent k-itemsets containing only values of categorical attributes and then quantitative attributes are processed one by one. Discretization of values during quantitative attributes processing is based on a new measure called average distance.

BibTex


@inproceedings{BUT13997,
  author="Vladimír {Bartík}",
  title="Average Distance Based Method For Association Rules Discovery In Relational Databases",
  annote="The paper describes the new method for association rule discovery in
relational databases, which contain quantitative and categorical
attributes. Many methods have been developed yet. But most of these
methods are based on initial equi-depth discretization of quantitative
attributes. These approaches bring the loss of information. The basic
idea of the new method is to separate processing of categorical and
quantitative attributes. The first step finds frequent k-itemsets
containing only values of categorical attributes and then quantitative
attributes are processed one by one. Discretization of values during
quantitative attributes processing is based on a new measure called
average distance.",
  booktitle="Proceedings of 6th International Conference ISIM'03 - Information Systems Implementation and Modelling",
  chapter="13997",
  year="2003",
  month="april",
  pages="161--168",
  type="conference paper"
}