pm4py.algo.clustering.trace_attribute_driven package

Submodules

pm4py.algo.clustering.trace_attribute_driven.algorithm module

class pm4py.algo.clustering.trace_attribute_driven.algorithm.Variants(value)[source]

Bases: enum.Enum

An enumeration.

DFG = <module 'pm4py.algo.clustering.trace_attribute_driven.dfg.dfg_dist' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\clustering\\trace_attribute_driven\\dfg\\dfg_dist.py'>
VARIANT_AVG_LEVEN(percent, alpha)
VARIANT_AVG_VEC(percent, alpha)
VARIANT_DMM_LEVEN(percent, alpha)
VARIANT_DMM_VEC(percent, alpha)
pm4py.algo.clustering.trace_attribute_driven.algorithm.apply(log, trace_attribute, variant=<function eval_DMM_leven>, parameters=None)[source]

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

pm4py.algo.clustering.trace_attribute_driven.algorithm.bfs(tree)[source]

pm4py.algo.clustering.trace_attribute_driven.parameters module

class pm4py.algo.clustering.trace_attribute_driven.parameters.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
ATTRIBUTE_KEY = 'pm4py:param:attribute_key'
BINARIZE = 'binarize'
LOWER_PERCENT = 'lower_percent'
POSITIVE = 'positive'
SINGLE = 'single'

Module contents