Sequential pattern mining algorithm for automotive warranty data

This paper presents a sequential pattern mining algorithm that allows product and quality engineers to extract hidden knowledge from a large automotive warranty database. The algorithm uses the elementary set concept and database manipulation techniques to search for patterns or relationships among occurrences of warranty claims over time. These patterns are represented as IF–THEN sequential rules, where the IF portion of the rule includes one or more occurrences of warranty problems at one time and the THEN portion includes warranty problem(s) that occur at a later time. Once sequential patterns are generated, the algorithm uses rule strength parameters to filter out insignificant patterns, so that only important (significant) rules are reported. Significant patterns provide knowledge of one or more product failures that leads to future product fault(s). The effectiveness of the algorithm is illustrated with the warranty data mining application from the automotive industry. A discussion on the sequential patterns generated by the algorithm and their interpretation for the automotive example are also provided.