pm4py.algo.discovery.correlation_mining.variants.classic_split module#

class pm4py.algo.discovery.correlation_mining.variants.classic_split.Parameters(*values)[source]#

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
SAMPLE_SIZE = 'sample_size'#
pm4py.algo.discovery.correlation_mining.variants.classic_split.apply(log: EventLog | EventStream | DataFrame, parameters: Dict[str | Parameters, Any] | None = None) → Tuple[Dict[Tuple[str, str], int], Dict[Tuple[str, str], float]][source]#

Applies the correlation miner (splits the log in smaller chunks)

Parameters:

  • log – Log object

  • parameters – Parameters of the algorithm

Returns:

  • dfg – Frequency DFG

  • performance_dfg – Performance DFG