Knowledge Discovery And Supervised Machine Learning In A Construction Project Database

Abstract

The construction industry is experiencing explosive growth in its capability to generate and collect data. Advances in data storage technology have allowed the transformation of an enormous amount of data into computerized database systems. Nowadays, there are many efforts to convert the large amounts of data into useful patterns or trends. Knowledge Discovery in Database (KDD) is a process that combines Data Mining (DM) techniques from machine learning, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from a large database. By applying KDD and DM to the analysis of construction project data, this paper presents the results of a research that discovers the knowledge through KDD process to better identify recurring construction problems.