View source for PM: Methods in pm4py

Here’s a '''comparison table of the main methods in process mining (as available in PM4Py)''' so you can see their differences at a glance:

==Process Discovery Methods==


{| class="wikitable"
|-
! '''Method'''           !! '''Output Model'''                !! '''Pros'''                                       !! '''Cons'''                                !! '''Best Use Case'''
|-
| '''Alpha Miner'''      || Petri Net                       || Simple, foundational, easy to explain          || Very sensitive to noise/incomplete logs || Educational/demo purposes, very clean logs
|-
| '''Heuristics Miner''' || Heuristics Net / Petri Net      || Handles noise, considers frequency             || May oversimplify rare behavior          || Real-life logs with noise and high variability
|-
| '''Inductive Miner'''  || Petri Net / Process Tree / BPMN || Always produces sound models, block-structured || May abstract away some detail           || General-purpose discovery, recommended default
|-
| '''ILP Miner'''        || Petri Net                       || Precise, mathematically grounded               || Heavy computational cost                || Small/medium logs where precision is critical
|-
| '''DFG Discovery'''    || Directly-Follows Graph          || Very fast, intuitive visualization             || Lacks formal semantics, not executable  || Quick insights, dashboards
|}



==Conformance Checking Methods==

{| class="wikitable"
|-
! '''Method'''                   !! '''Pros'''                            !! '''Cons'''                                  !! '''Best Use Case'''                    
|-
| '''Token-Based Replay'''       || Fast, intuitive, easy to compute    || Less precise, may misrepresent deviations || Quick conformance estimation
|-
| '''Alignment-Based Checking''' || Very precise, finds optimal matches || Computationally expensive for large logs  || Audit scenarios, compliance checking
|-
| '''Log Skeleton'''             || Lightweight, structural conformance || Not as expressive as Petri net alignments || Quick structural validation
|}



==Performance Analysis==

{| class="wikitable"
! '''Technique'''                  !! '''Pros'''                                  !! '''Cons'''                        !! '''Best Use Case'''
|-
| '''Sojourn / throughput times''' || Easy to interpret, highlights bottlenecks || Needs reliable timestamp data   || Detecting slow activities
|-
| '''Time annotations on arcs'''   || Visual enrichment of models               || Only as good as the log quality || Identifying bottlenecks in process paths
|-
| '''Case duration analysis'''     || Summarizes case lifetimes                 || Doesn’t explain internal causes || SLA monitoring
|}

==Other Techniques==

{| class="wikitable"
! '''Method'''                         !! '''Pros'''                                !! '''Cons'''                                       !! '''Best Use Case'''
|-
| '''Trace Variants Analysis'''        || Simple, shows different execution paths || Can explode with many variants                 || Exploratory analysis
|-
| '''Trace Clustering'''               || Groups similar behaviors                || Choice of clustering algorithm impacts results || Finding behavior patterns
|-
| '''Predictive Monitoring (via ML)''' || Anticipates outcomes, remaining time    || Needs feature engineering, external ML models  || Predictive SLA, early-warning systems
|}


=='''Key Takeaway:'''==

* If you want '''robust discovery''' → use '''Inductive Miner'''.
* If you need '''fast visualization''' → use '''DFG Discovery'''.
* For '''compliance checks''' → prefer '''Alignment-based Conformance'''.
* For '''real-life noisy data''' → '''Heuristics Miner''' is strong.