pm4py.algo.clustering.trace_attribute_driven package

PM4Py – A Process Mining Library for Python

Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

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

Subpackages

• pm4py.algo.clustering.trace_attribute_driven.dfg package
  • Submodules
  • pm4py.algo.clustering.trace_attribute_driven.dfg.dfg_dist module
    • dfg_dist_calc_act()
    • dfg_dist_calc_suc()
    • dfg_dist_calc()
    • dfg_dist_calc_minkowski()
• pm4py.algo.clustering.trace_attribute_driven.leven_dist package
  • Submodules
  • pm4py.algo.clustering.trace_attribute_driven.leven_dist.leven_dist_calc module
    • leven_preprocess()
    • leven_dist()
    • leven_dist_avg()
• pm4py.algo.clustering.trace_attribute_driven.linkage_method package
  • Submodules
  • pm4py.algo.clustering.trace_attribute_driven.linkage_method.linkage_avg module
    • linkage_dfg_update()
    • linkage_avg()
    • linkage_DMM_update()
    • linkage_DMM_update_leven()
• pm4py.algo.clustering.trace_attribute_driven.merge_log package
  • Submodules
  • pm4py.algo.clustering.trace_attribute_driven.merge_log.merge_log module
    • merge_log()
    • update_merge()
    • log2sublog()
    • logslice()
    • add_node()
    • label_tree()
    • clusteredlog()
• pm4py.algo.clustering.trace_attribute_driven.util package
  • Submodules
  • pm4py.algo.clustering.trace_attribute_driven.util.evaluation module
    • dfg_dis()
    • eval_avg_variant()
    • eval_DMM_variant()
    • eval_avg_leven()
    • eval_DMM_leven()
  • pm4py.algo.clustering.trace_attribute_driven.util.filter_subsets module
    • Parameters
      • Parameters.ATTRIBUTE_KEY
      • Parameters.ACTIVITY_KEY
      • Parameters.SINGLE
      • Parameters.BINARIZE
      • Parameters.POSITIVE
      • Parameters.LOWER_PERCENT
    • apply_trace_attributes()
    • sublog2varlist()
    • sublog_percent()
    • sublog_percent2actlist()
    • sublog_percent2varlist()
    • logslice_percent_act()
    • apply_variants_filter()
    • logslice_percent()
    • sublog2df_num()
    • sublog2df()
    • act_dist()
• pm4py.algo.clustering.trace_attribute_driven.variants package
  • Submodules
  • pm4py.algo.clustering.trace_attribute_driven.variants.act_dist_calc module
    • Parameters
      • Parameters.ATTRIBUTE_KEY
      • Parameters.ACTIVITY_KEY
      • Parameters.SINGLE
      • Parameters.BINARIZE
      • Parameters.POSITIVE
      • Parameters.LOWER_PERCENT
    • occu_var_act()
    • act_sim()
    • act_sim_med()
    • act_sim_dual()
    • act_sim_percent()
    • act_sim_percent_avg()
    • act_sim_percent_avg_actset()
  • pm4py.algo.clustering.trace_attribute_driven.variants.logslice_dist module
    • log2sublog()
    • slice_dist_suc()
    • slice_dist_act()
  • pm4py.algo.clustering.trace_attribute_driven.variants.sim_calc module
    • Parameters
      • Parameters.ATTRIBUTE_KEY
      • Parameters.ACTIVITY_KEY
      • Parameters.SINGLE
      • Parameters.BINARIZE
      • Parameters.POSITIVE
      • Parameters.LOWER_PERCENT
    • inner_prod_calc()
    • dist_calc()
  • pm4py.algo.clustering.trace_attribute_driven.variants.suc_dist_calc module
    • Parameters
      • Parameters.ATTRIBUTE_KEY
      • Parameters.ACTIVITY_KEY
      • Parameters.SINGLE
      • Parameters.BINARIZE
      • Parameters.POSITIVE
      • Parameters.LOWER_PERCENT
    • occu_suc()
    • occu_var_suc()
    • suc_sim()
    • suc_sim_dual()
    • suc_sim_percent()
    • suc_sim_percent_avg()

Submodules

pm4py.algo.clustering.trace_attribute_driven.algorithm module

PM4Py – A Process Mining Library for Python

Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

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

class pm4py.algo.clustering.trace_attribute_driven.algorithm.Variants(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

VARIANT_DMM_LEVEN(percent, alpha)

VARIANT_AVG_LEVEN(percent, alpha)

VARIANT_DMM_VEC(percent, alpha)

VARIANT_AVG_VEC(percent, alpha)

DFG = <module 'pm4py.algo.clustering.trace_attribute_driven.dfg.dfg_dist' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\clustering\\trace_attribute_driven\\dfg\\dfg_dist.py'>

pm4py.algo.clustering.trace_attribute_driven.algorithm.bfs(tree)

pm4py.algo.clustering.trace_attribute_driven.algorithm.apply(log: ~pm4py.objects.log.obj.EventLog | ~pm4py.objects.log.obj.EventStream | ~pandas.core.frame.DataFrame, trace_attribute: str, variant=<function eval_DMM_leven>, parameters: ~typing.Dict[~typing.Any, ~typing.Any] | None = None) → Any

Apply the hierarchical clustering to a log starting from a trace attribute.

MSc Thesis is available at: https://www.pads.rwth-aachen.de/global/show_document.asp?id=aaaaaaaaalpxgft&download=1 Defense slides are available at: https://www.pads.rwth-aachen.de/global/show_document.asp?id=aaaaaaaaalpxgqx&download=1

Parameters

log

Log

trace_attribute

Trace attribute to exploit for the clustering

variant

Variant of the algorithm to apply, possible values: - Variants.VARIANT_DMM_LEVEN (that is the default) - Variants.VARIANT_AVG_LEVEN - Variants.VARIANT_DMM_VEC - Variants.VARIANT_AVG_VEC - Variants.DFG

Returns

tree

Hierarchical cluster tree

leafname

Root node