1. A computer-implemented process discovery method comprising: receiving as input at least one noisy log file that contains a plurality of labeled log traces from a plurality of process models; clustering similar log traces using non-negative matrix factorization (NMF) into a plurality of clusters, wherein each cluster represents a different process model; learning one or more Conditional Random Field (CRF) models for each of the process models; decoding new incoming log traces; and constructing a tunable process graph, wherein one or more transitions are shown or hidden according to a tuning parameter, wherein clustering similar log traces further comprises decomposing a term-document matrix into at least a term-cluster matrix and a cluster-document matrix.