Machine learning is concerned with enabling computer programs automatically to improve their performance at some tasks through experience. Manufacturing is an area where the application of machine learning can be very fruitful. However, little has been published about the use of machine-learning techniques in the manufacturing domain. This paper evaluates several machine-learning techniques and examines applications in which they have been successfully deployed. Special attention is given to inductive learning, which is among the most mature of the machine-learning approaches currently available. Current trends and recent developments in machine-learning research are also discussed. The paper concludes with a summary of some of the key research issues in machine learning.

Clustering is an important data exploration technique with many applications in different areas of engineering, including engineering design, manufacturing system design, quality assurance, production planning and process planning, modelling, monitoring, and control. The clustering problem has been addressed by researchers from many disciplines. However, efforts to perform effective and efficient clustering on large data sets only started in recent years with the emergence of data mining. The current paper presents an overview of clustering algorithms from a data mining perspective. Attention is paid to techniques of scaling up these algorithms to handle large data sets. The paper also describes a number of engineering applications to illustrate the potential of clustering algorithms as a tool for handling complex real-world problems.

Rule induction as a method for constructing classifiers is particularly attractive in data mining applications, where the comprehensibility of the generated models is very important. Most existing techniques were designed for small data sets and thus are not practical for direct use on very large data sets because of their computational inefficiency. Scaling up rule induction methods to handle such data sets is a formidable challenge. This article presents a new algorithm for rule induction that can efficiently extract accurate and comprehensible models from large and noisy data sets. This algorithm has been tested on several complex data sets, and the results prove that it scales up well and is an extremely effective learner.

RULES-3 Plus is a member of the RULES family of simple inductive learning algorithms with successful engineering applications. However, it requires modification in order to be a practical tool for problems involving large data sets. In particular, efficient mechanisms are needed for handling continuous attributes and noisy data. This article presents a new rule induction algorithm called RULES-6, which is derived from the RULES-3 Plus algorithm. The algorithm employs a fast and noise-tolerant search method for extracting IF-THEN rules from examples. It also uses simple and effective methods for rule evaluation and handling of continuous attributes. A detailed empirical evaluation of the algorithm is reported in this paper. The results presented demonstrate the strong performance of the algorithm.

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