BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A robust and scalable clustering algorithm for mixed type attributes in large database environment
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Constraint-based clustering and its applications in construction management
Expert Systems with Applications: An International Journal
SpectralCAT: Categorical spectral clustering of numerical and nominal data
Pattern Recognition
Review: Data mining techniques and applications - A decade review from 2000 to 2011
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The purpose of this paper is to characterize the competitive positioning of the construction industry companies and evaluate their financial performance. The methodology proposed involves three major stages. The first stage concerns the identification of the competitive positioning of companies within the construction sector. This is achieved using a hierarchical clustering algorithm suitable for large datasets and mixed type variables. The second stage is the analysis of performance of the different clusters. This is done using the Data Envelopment Analysis technique. To characterize in detail the main performance features of each cluster, a decision tree is used to extract the main rules concerning the performance spread within each cluster and the gap between the cluster best practices and the national benchmarks. The third stage concerns the analysis of the strengths, weaknesses and areas of potential improvement for contractors in each competitive positioning. This required the analysis of benchmark companies of each cluster. The methodology proposed was applied for the analysis of performance of all contractors that operate in the Portuguese construction industry.