pm4py.algo.discovery.powl.inductive.variants.dynamic_clustering_frequency 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.discovery.powl.inductive.variants.dynamic_clustering_frequency.dynamic_clustering_frequency_partial_order_cut 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

pm4py.algo.discovery.powl.inductive.variants.dynamic_clustering_frequency.dynamic_clustering_frequency_partial_order_cut.generate_order(obj: T, clusters, order_frequency_ratio)[source]#

pm4py.algo.discovery.powl.inductive.variants.dynamic_clustering_frequency.dynamic_clustering_frequency_partial_order_cut.compute_efg_frequencies(interval_log: IMDataStructureUVCL, groups) → Dict[Tuple[str, str], int][source]#

class pm4py.algo.discovery.powl.inductive.variants.dynamic_clustering_frequency.dynamic_clustering_frequency_partial_order_cut.DynamicClusteringFrequencyPartialOrderCut[source]#

Bases: Cut[T], ABC, Generic[T]

classmethod operator(parameters: Dict[str, Any] | None = None) → StrictPartialOrder[source]#

classmethod holds(obj: T, parameters: Dict[str, Any] | None = None) → BinaryRelation | None[source]#

classmethod apply(obj: T, parameters: Dict[str, Any] | None = None) → Tuple[StrictPartialOrder, List[POWL]] | None[source]#

class pm4py.algo.discovery.powl.inductive.variants.dynamic_clustering_frequency.dynamic_clustering_frequency_partial_order_cut.DynamicClusteringFrequencyPartialOrderCutUVCL[source]#

Bases: DynamicClusteringFrequencyPartialOrderCut[IMDataStructureUVCL]

classmethod project(obj: IMDataStructureUVCL, groups: List[Collection[Any]], parameters: Dict[str, Any] | None = None) → List[IMDataStructureUVCL][source]#: Projection of the given data object (Generic type T). Returns a corresponding process tree and the projected sub logs according to the identified groups. A precondition of the project function is that it holds on the object for the given Object

pm4py.algo.discovery.powl.inductive.variants.dynamic_clustering_frequency.factory 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.discovery.powl.inductive.variants.dynamic_clustering_frequency.factory.CutFactoryPOWLDynamicClusteringFrequency[source]#

Bases: CutFactory

classmethod get_cuts(obj, parameters=None)[source]#

classmethod find_cut(obj: IMDataStructure, parameters: Dict[str, Any] | None = None) → Tuple[POWL, List[T]] | None[source]#