pm4py.algo.clustering.profiles.variants package#

PM4Py – A Process Mining Library for Python

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

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

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

class pm4py.algo.clustering.profiles.variants.sklearn_profiles.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[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: Generator of logs (clusters)