Publication detail

Mining Association Rules from Relational Data - Average Distance Based Method

BARTÍK, V., ZENDULKA, J.

Original Title

Mining Association Rules from Relational Data - Average Distance Based Method

English Title

Mining Association Rules from Relational Data - Average Distance Based Method

Type

journal article - other

Language

en

Original Abstract

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.

English abstract

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.

Keywords

association rule, frequent itemset, categorical attribute, quantitative attribute

RIV year

2003

Released

01.11.2003

ISBN

0302-9743

Periodical

Lecture Notes in Computer Science

Year of study

2003

Number

2888

State

DE

Pages from

757

Pages to

766

Pages count

10

Documents

BibTex


@article{BUT41989,
  author="Vladimír {Bartík} and Jaroslav {Zendulka}",
  title="Mining Association Rules from Relational Data - Average Distance Based Method",
  annote="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.",
  chapter="41989",
  journal="Lecture Notes in Computer Science",
  number="2888",
  volume="2003",
  year="2003",
  month="november",
  pages="757--766",
  type="journal article - other"
}