Decision support for improved service effectiveness using domain aware text mining
This paper presents a decision support system Domain Aware Text & Association Mining (DATAM) which has been developed to improve after-sales service and repairs for the automotive domain. A novel approach that compares textual and non-textual data for anomaly detection is proposed. It combines association and ontology based text mining. Association mining has been employed to identify the repairs performed in the field for a given symptom, whereas, text mining is used to infer repairs from the textual instructions mentioned in service documents for the same symptom. These in turn are compared and contrasted to identify the anomalous cases. The developed approach has been applied to automotive field data. Using the top 20 most frequent symptoms, observed in a mid-sized sedan built and sold in North America, it is demonstrated that DATAM can identify all the anomalous symptom  repair code combinations (with a false positive rate of 0.04). This knowledge, in the form of anomalies, can subsequently be used to improve the service/trouble-shooting procedure and identify technician training needs.
text mining decision support systems association mining anomaly detection semantic text analysis