Alignments provide a robust approach for conformance checking, which has been largely applied in various contexts such as auditing and performance analysis. Alignment-based conformance checking techniques pinpoint the deviations causing nonconformity based on a cost function. However, such a cost function is often manually defined on the basis of human judgment and thus error-prone, leading to alignments that do not provide accurate explanations of nonconformity. This paper proposes an approach to automatically define the cost function based on information extracted from the past process executions. The cost function only relies on objective factors and thus enables the construction of probable alignments, i.e. alignments that provide probable explanations of nonconformity. Our approach has been implemented in ProM and evaluated using both synthetic and real-life data.