pm4py.algo.clustering.profiles.variants.sklearn_profiles module#

class pm4py.algo.clustering.profiles.variants.sklearn_profiles.Parameters(*values)[source]#

Bases: Enum

SKLEARN_CLUSTERER = 'sklearn_clusterer'#
pm4py.algo.clustering.profiles.variants.sklearn_profiles.apply(log: EventLog | EventStream | DataFrame, parameters: Dict[Any, Any] | None = None) → Generator[EventLog, None, None][source]#

Cluster the event log, based on the extraction of profiles for the traces of the event log (by means of the feature extraction proposed in pm4py) and the application of a Scikit learn clusterer (default: K-means with two clusters)

Implements the approach described in: Song, Minseok, Christian W. Günther, and Wil MP Van der Aalst. “Trace clustering in process mining.” Business Process Management Workshops: BPM 2008 International Workshops, Milano, Italy, September 1-4, 2008. Revised Papers 6. Springer Berlin Heidelberg, 2009.

Parameters:

  • log – Event log

  • parameters – Parameters of the feature extraction, including: - Parameters.SKLEARN_CLUSTERER => the Scikit-Learn clusterer to be used (default: KMeans(n_clusters=2, random_state=0, n_init=”auto”))

Returns:

Generator of logs (clusters)

Return type:

generator