pm4py.algo.discovery.correlation_mining.algorithm module#

PM4Py – A Process Mining Library for Python Copyright (C) 2026 Process Intelligence Solutions GmbH

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class pm4py.algo.discovery.correlation_mining.algorithm.Variants(*values)[source]#

Bases: Enum

CLASSIC_SPLIT = <module 'pm4py.algo.discovery.correlation_mining.variants.classic_split' from '/home/javert/ALE/pm4py/pm4py/algo/discovery/correlation_mining/variants/classic_split.py'>#
CLASSIC = <module 'pm4py.algo.discovery.correlation_mining.variants.classic' from '/home/javert/ALE/pm4py/pm4py/algo/discovery/correlation_mining/variants/classic.py'>#
TRACE_BASED = <module 'pm4py.algo.discovery.correlation_mining.variants.trace_based' from '/home/javert/ALE/pm4py/pm4py/algo/discovery/correlation_mining/variants/trace_based.py'>#
pm4py.algo.discovery.correlation_mining.algorithm.apply(log: EventLog | EventStream | DataFrame, variant=Variants.CLASSIC, parameters: Dict[Any, Any] | None = None) → Tuple[Dict[Tuple[str, str], int], Dict[Tuple[str, str], float]][source]#

Applies the Correlation Miner to the event stream (a log is converted to a stream)

The approach is described in: Pourmirza, Shaya, Remco Dijkman, and Paul Grefen. “Correlation miner: mining business process models and event correlations without case identifiers.” International Journal of Cooperative Information Systems 26.02 (2017): 1742002.

Parameters:

  • log – Log object

  • variant – Variant of the algorithm to use

  • parameters – Parameters of the algorithm

Returns:

  • dfg – Directly-follows graph

  • performance_dfg – Performance DFG (containing the estimated performance for the arcs)