Author details

Sien Chen1,2,3\*

\*Address all correspondence to: sien.chen@postgrad.manchester.ac.uk

1 Institute of Internet Industry, Tsinghua University, Beijing, China

2 Alliance Manchester Business School, University of Manchester, Manchester, UK

3 Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China

### References


[10] Blattberg EC, Malthouse FJ. Can we predict customer lifetime value? Journal of Interactive Marketing. 2005;19(1):2-16

estimation research. After that, a numeral case of China Eastern Airlines was given to show the practicability and veracity of China Eastern Airlines passenger network value assessment model with assessing their fitting accuracy rate with the TravelSky value score. The ambition is combining forecast value score calculated by China Eastern Airlines passenger network

value assessment model with loss model score to select the critical population.

\*Address all correspondence to: sien.chen@postgrad.manchester.ac.uk

2 Alliance Manchester Business School, University of Manchester, Manchester, UK

3 Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai,

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1 Institute of Internet Industry, Tsinghua University, Beijing, China

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Author details

Sien Chen1,2,3\*

32 Data Mining

China

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**Chapter 3**

**Provisional chapter**

**Determination and Classification of Crew Productivity**

**Determination and Classification of Crew Productivity** 

Turkey is a developing country and the main axis of development is "construction." The construction sector is in a position to create demand for goods and services produced by more than 200 subsectors, and this widespread impact is the most basic indicator of the sector's "locomotive of the economy." In the development of the construction industry, crew productivity plays a very important role. While businesses that do not measure their employees' needs, their locations, and so on are suffering from various losses, rare businesses that take these parameters into account can profit. The identification of leadership types that will motivate employees has great importance in terms of construction businesses where the human element is the foreground. For this purpose, in the province of Adana, the relationship of productivity between the engineers working in construction companies and workers who work at lower departments of these engineers was examined. In this study, bidirectional multiple leadership questionnaire (MLQ) was applied to construction site managers and employees, and according to this survey data, leadership and motivations/productivities were classified using data mining methods. According to the classification analysis results, the most successful data mining algorithm was random

**Keywords:** classification, construction information, construction management, crew productivity, data mining, random forest algorithm, supervised and unsupervised

With the increasing globalization in the construction sector, institutionalization is at the forefront. In addition, under increasing competition conditions, construction companies

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: 10.5772/intechopen.75504

**with Data Mining Methods**

**with Data Mining Methods**

http://dx.doi.org/10.5772/intechopen.75504

**Abstract**

learning, Weka

**1. Introduction**

Abdullah Emre Keleş and Mümine Kaya Keleş

Abdullah Emre Keleş and Mümine Kaya Keleş

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

forest algorithm with a rate of 81.3725%.


### **Determination and Classification of Crew Productivity with Data Mining Methods Determination and Classification of Crew Productivity with Data Mining Methods**

DOI: 10.5772/intechopen.75504

Abdullah Emre Keleş and Mümine Kaya Keleş Abdullah Emre Keleş and Mümine Kaya Keleş

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.75504

### **Abstract**

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Turkey is a developing country and the main axis of development is "construction." The construction sector is in a position to create demand for goods and services produced by more than 200 subsectors, and this widespread impact is the most basic indicator of the sector's "locomotive of the economy." In the development of the construction industry, crew productivity plays a very important role. While businesses that do not measure their employees' needs, their locations, and so on are suffering from various losses, rare businesses that take these parameters into account can profit. The identification of leadership types that will motivate employees has great importance in terms of construction businesses where the human element is the foreground. For this purpose, in the province of Adana, the relationship of productivity between the engineers working in construction companies and workers who work at lower departments of these engineers was examined. In this study, bidirectional multiple leadership questionnaire (MLQ) was applied to construction site managers and employees, and according to this survey data, leadership and motivations/productivities were classified using data mining methods. According to the classification analysis results, the most successful data mining algorithm was random forest algorithm with a rate of 81.3725%.

**Keywords:** classification, construction information, construction management, crew productivity, data mining, random forest algorithm, supervised and unsupervised learning, Weka
