MINING QUANTITATIVE ASSOCIATION RULES IN MICROARRAY DATA USING EVOLUTIVE ALGORITHMS

被引：0

作者：

Martinez-Ballesteros, M. ^{[1
]}

Rubio-Escudero, C. ^{[1
]}

Riquelme, J. C. ^{[1
]}

Martinez-Alvarez, F. ^{[2
]}

机构：

[1] Univ Seville, Dept Comp Sci, Seville, Spain

[2] Pablo Olavide Univ Seville, Dept Comp Sci, Seville, Spain

来源：

ICAART 2011: PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1 | 2011年

关键词：

Data mining; Evolutionary algorithms; Quantitative association rules; MicroArray;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing data from such experiments, which are characterized by the high number of genes to be analyzed in relation to the low number of experiments or samples available. In this paper we show the result of applying a data mining method based on quantitative association rules for microarray data. These rules work with intervals on the attributes, without discretizing the data before. The rules are generated by an evolutionary algorithm.

引用

页码：574 / 577

页数：4

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