Using Text Mining Methods for Analysis of Production Data in Automotive Industry

被引：2

作者：

Hrcka, Lukas ^{[1
,2
]}

Simoncicova, Veronika ^{[1
,2
]}

Tadanai, Ondrej ^{[1
,2
]}

Tanuska, Pavol ^{[1
,2
]}

Vazan, Pavel ^{[1
,2
]}

机构：

[1] Slovak Univ Technol, Fac Mat Sci & Technol, Trnava, Slovakia

[2] Inst Appl Informat Automat & Mechatron, Trnava, Slovakia

来源：

ARTIFICIAL INTELLIGENCE TRENDS IN INTELLIGENT SYSTEMS, CSOC2017, VOL 1 | 2017年 / 573卷

关键词：

RapidMiner; Text mining; Analysis; Data; Information;

D O I：

10.1007/978-3-319-57261-1_39

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Text mining is the process of extracting useful and high-quality information from unstructured textual data through the identification and exploration of interesting patterns. Text mining also referred to as text data mining, roughly equivalent to text analytics. RapidMiner is unquestionably the world-leading open-source system for this analytics, it is the most powerful and easy to use. Acquiring information from text is a requested area of research in automotive industry. This paper aims at presenting the use of text mining in this industry field. The article is focused on working with text attributes "ResponsibleEmp", which is crucial for text mining analysis. The outcome of this article is the number of breakdowns and name of a specific employee responsible for breakdowns. The presented analysis is provided as a partial result of the research and will serve to further investigation in the problem area.

引用

页码：393 / 403

页数：11