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A non-compensatory approach for trace clustering

Delias Pavlos, Doumpos Michael, Grigoroudis Evangelos, Matsatsinis Nikolaos

Πλήρης Εγγραφή

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Περίληψη

One of the main functions of process mining is the automated discovery of process models from event log files. However, in flexible environments, such as healthcare or customer service, delivering comprehensible process models can be very challenging, mainly due to the complexity of the registered logs. A prevalent response to this problem is trace clustering, that is, grouping behaviors and discovering a distinct model per group. In this paper, we propose a novel trace clustering technique inspired from the outranking relations theory. The proposed technique can handle multiple criteria with strongly heterogeneous scales, and it allows a non-compensatory logic to guide the creation of a similarity metric. To reach this, we use three key components: We separate factors that are in favor of the similarity from those that are not, through discrimination thresholds; we provide non-concordant factors with a "veto" power; and we aggregate all factors into an overall metric. We evaluated this novel, non-compensatory approach against two of the most spotlighted trace clustering functions: variants' identification and model complexity reduction. Results suggest that the proposed technique can be used at both functions with compelling performance.

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