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The aim of process discovery is to build a process model from an event log without prior information about the process. The discovery of declarative process models is useful when a process works in an unpredictable and unstable environment since several allowed paths can be represented as a compact set of rules. One of the tools available in the literature for discovering declarative models from logs is the Declare Miner, a plug-in of the process mining tool ProM. Using this plug-in, the discovered models are represented using Declare, a declarative process modelling language based on LTL for finite traces. In this paper, we use a combination of an Apriori algorithm and a group of algorithms for Sequence Analysis to improve the performances of the Declare Miner. Using synthetic and real life event logs, we show that the new implemented core of the plug-in allows for a significant performance improvement.

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