PDC 2020

Comparing process discovery algorithms

  • Overview
  • Data Sets
  • Miners
Comparing process discovery algorithms

Classification

Classification of Split Miner on PDC 2020 data set

August 27, 2019 by Eric Leave a Comment

These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 500 traces are TP in the first run, 490 in the second, and 495 in the third, then the value 490 is used as aggregated … [Read more…]

Posted in: Classification, PDC 2020, Split Miner

Classification of Split Miner on PDC 2019 data set

August 27, 2019 by Eric Leave a Comment

These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 45 traces are TP in the first run, 40 in the second, and 42 in the third, then the value 40 is used as aggregated … [Read more…]

Posted in: Classification, PDC 2019, Split Miner

Classification of Split Miner on PDC 2017 data set

August 27, 2019 by Eric Leave a Comment

These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 10 traces are TP in the first run, 8 in the second, and 9 in the third, then the value 8 is used as aggregated … [Read more…]

Posted in: Classification, PDC 2017, Split Miner

Classification of Split Miner on PDC 2016 data set

August 27, 2019 by Eric Leave a Comment

These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 10 traces are TP in the first run, 8 in the second, and 9 in the third, then the value 8 is used as aggregated … [Read more…]

Posted in: Classification, PDC 2016, Split Miner

Classification of Split Miner on aXfYnZ data set

August 27, 2019 by Eric Leave a Comment

These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 500 traces are TP in the first run, 490 in the second, and 495 in the third, then the value 490 is used as aggregated … [Read more…]

Posted in: aXfYnZ, Classification, Split Miner

Classification of Log Skeleton (5% noise) on PDC 2020 data set

August 27, 2019 by Eric Leave a Comment

These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 500 traces are TP in the first run, 490 in the second, and 495 in the third, then the value 490 is used as aggregated … [Read more…]

Posted in: Classification, Log Skeleton (5% noise), PDC 2020

Classification of Log Skeleton (5% noise) on PDC 2019 data set

August 27, 2019 by Eric Leave a Comment

These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 500 traces are TP in the first run, 490 in the second, and 495 in the third, then the value 490 is used as aggregated … [Read more…]

Posted in: Classification, Log Skeleton (5% noise), PDC 2019

Classification of Log Skeleton (5% noise) on PDC 2017 data set

August 27, 2019 by Eric Leave a Comment

These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 10 traces are TP in the first run, 8 in the second, and 9 in the third, then the value 8 is used as aggregated … [Read more…]

Posted in: Classification, Log Skeleton (5% noise), PDC 2017

Classification of Log Skeleton (5% noise) on PDC 2016 data set

August 27, 2019 by Eric Leave a Comment

These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 10 traces are TP in the first run, 8 in the second, and 9 in the third, then the value 8 is used as aggregated … [Read more…]

Posted in: Classification, Log Skeleton (5% noise), PDC 2016

Classification of Log Skeleton (5% noise) on aXfYnZ data set

August 27, 2019 by Eric Leave a Comment

These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 500 traces are TP in the first run, 490 in the second, and 495 in the third, then the value 490 is used as aggregated … [Read more…]

Posted in: aXfYnZ, Classification, Log Skeleton (5% noise)
1 2 3 4 Next »

Recent Posts

  • F-score of all miners on PDC 2020 data set
  • F-score of Alpha Miner on PDC 2020 data set
  • F-score of Fodina Miner on PDC 2020 data set
  • F-score of Hybrid ILP Miner on PDC 2020 data set
  • F-score of Inductive Miner on PDC 2020 data set

Recent Comments

    Archives

    • January 2020
    • September 2019
    • August 2019
    • July 2019

    Categories

    • Data Set
      • aXfYnZ
      • PDC 2016
      • PDC 2017
      • PDC 2019
      • PDC 2020
    • Metric
      • Accuracy
      • Classification
      • F-score
    • Miner
      • All Miners
      • Alpha Miner
      • Fodina Miner
      • Hybrid ILP Miner
      • Inductive Miner
      • Inductive Miner (OR)
      • Log Skeleton
      • Log Skeleton (5% noise)
      • Split Miner

    Meta

    • Log in
    • Entries RSS
    • Comments RSS
    • WordPress.org

    Copyright © 2023 PDC 2020.

    Church WordPress Theme by themehall.com