Applying data mining to a field quality watchdog task

This article describes a watchdog program that discovers “meaningful” repair cases from a field service database. “Meaningful” cases are those judged worth probing further to prevent an epidemic of quality problems. Our system has employed the apriori algorithm, a data mining technique that efficiently performs the basket analysis. Our system proves that this data mining technique is not only useful in knowledge discovery but is also capable of performing the database watchdog task. The apriori algorithm automatically generates frequent itemsets from a large set of records. A frequent itemset is an arbitrary combination of values that appear more often than a threshold “minimum support.” The algorithm often generates too many itemsets for quality engineers to review carefully in their daily work. Many itemsets do not provide sufficient information to investigate further. Hence, in order not to generate these valueless itemsets, the apriori algorithm is modified in two ways. One way is “basket analysis on objective and explanatory attributes” and the other is “itemset reduction.” The advantage of our method is demonstrated with some experimental results. © 2002 Wiley Periodicals, Inc. Electr Eng Jpn, 140(2): 18–25, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10034