DEA implementation and clustering analysis using the K-Means algorithm

被引：0

作者：

Lemos, CAA ^{[1
]}

Lins, MPE ^{[1
]}

Ebecken, NFF ^{[1
]}

机构：

[1] Univ Fed Rio de Janeiro, COPPE, BR-21941 Rio De Janeiro, Brazil

来源：

DATA MINING VI: DATA MINING, TEXT MINING AND THEIR BUSINESS APPLICATIONS | 2005年

关键词：

Data Envelopment Analysis; clustering; data mining; telecommunication quality indicator; decision support system;

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：

Nowadays, problems that involve efficiency analysis and decision support systems inside a company need special attention and a number of tools have been developed to support managers. DEA - Data Envelopment Analysis is one of these tools and its use is increasing in research and in new developments. The problem is how to improve the quality of DEA analysis when the DNM (decision-making unit) it analyzes is considered efficient, and how to guarantee the analysis if the input and output parameters that contain a lot of zeros? Probably these parameters have not been considered in how to visualize the inputs and outputs in n-dimensional space? This paper proposes combining another tool with DEA based in data mining, CLUSTERING, to evaluate the efficiency analyses made for DEA tools, and visualize groups which have inefficient DMUs, based on the K-Means algorithm, and apply over a telecommunication database that contains an indicator of efficiency of the telephone installation in the Brazilian market.

引用

页码：321 / 329

页数：9

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