Two-level clustering approach to training data instance selection: A case study for the steel industry

— Nowadays, huge amounts of information from different industrial processes are stored into databases and companies can improve their production efficiency by mining some new knowledge from this information. However, when these databases becomes too large, it is not efficient to process all the available data with practical data mining applications. As a solution, different approaches for intelligent selection of training data for model fitting have to be developed. In this article, training instances are selected to fit predictive regression models developed for optimization of the steel manufacturing process settings beforehand, and the selection is approached from a clustering point of view. Because basic k-means clustering was found to consume too much time and memory for the purpose, a new algorithm was developed to divide the data coarsely, after which k-means clustering could be performed. The instances were selected using the cluster structure by weighting more the observ...

• Type: Conference
• Year: 2008
• Where: IJCNN
• Authors: Heli Koskimäki, Ilmari Juutilainen, Perttu Laurinen, Juha Röning
• Keywords: Artificial Intelligence, IJCNN 2008, K-means Clustering, Practical Data Mining, Predictive Regression Models