## Classification of Split Miner on PDC 2020 data set

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…]

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

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…]

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

This graph shows the average accuracy values over three different runs for different categories of models. This graph shows the average accuracy values over three different runs for the situation without loops and with loops (simple or complex). For many logs without loops it holds that introducing simple loops lowers the accuracy, and introducing complex … [Read more…]

## Classification of Log Skeleton on PDC 2020 data set

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…]

## Classification of Inductive Miner (OR) on PDC 2020 data set

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…]

## Classification of Inductive Miner on PDC 2020 data set

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…]

## Classification of Hybrid ILP Miner on PDC 2020 data set

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…]

## Classification of Fodina Miner on PDC 2020 data set

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…]

## Classification of Alpha Miner on PDC 2020 data set

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…]

## Accuracy of Alpha Miner on PDC 2020 data set

This graph shows the average accuracy values over three different runs for different categories of models. This graph shows the average accuracy values over three different runs for the situation without optional tasks and with optional tasks. For example, it shows that if there are no optional tasks accuracy may be 100% (4 logs), but … [Read more…]