What are the 5 Best Process Mining Algorithms to Consider in 2024?

Process mining is an emerging data science technique that extracts insights from event log data to discover, monitor, and improve business processes. At its core, process mining relies on algorithms to automatically transform messy event data into structured process models.

But with a variety of algorithms to choose from, each with its own strengths and weaknesses, how do you know which one is the best fit for your needs?

In this comprehensive guide, we‘ll explore the top 5 process mining algorithms that should be on your radar going into 2023. I‘ll break down how each algorithm works under the hood, when it excels, and when alternative approaches might be better suited.

By the end, you‘ll understand the unique value proposition of algorithms like the Alpha miner, Heuristics miner, Fuzzy miner, Inductive miner, and Evolutionary tree miner. Let‘s dive in!

Overview of Process Mining Algorithms

Before diving into specifics, let‘s briefly explain what process mining algorithms do.

Process mining algorithms take as input event logs that track steps along the execution path of process instances. These event logs record activities with timestamps as cases flow through a business process.

The algorithms analyze these event logs to extract process models, showing the relationships, sequencing, and frequency statistics between events. Popular process modeling notations include BPMN, Petri nets, process trees, and dependency graphs.

The core challenge is that event logs provide only low-level activity traces, whereas process models provide a bird‘s eye view of the end-to-end process flow and structure. Algorithms need to bridge this gap using sophisticated statistical analysis and modeling techniques.

Different algorithms take different approaches based on aspects like:

  • Control-flow analysis – detecting sequential, parallel, and looping relationships
  • Abstraction – clustering low-level events into high-level activities
  • Conformance checking – measuring alignment between event log and process model
  • Optimization – evolving models to maximize fitness against the log

Now let‘s explore how 5 prominent process mining algorithms work and when each is most applicable.

1. Alpha Miner

The Alpha algorithm, developed by Professor Wil van der Aalst, is one of the most foundational process mining algorithms. Published in 2004, it was one of the first automated techniques for discovering a process model from an event log.

The Alpha miner takes the following approach:

  • Step 1 – Identify causality: Analyze the event log to determine causal relationships between events, based on their ordering and frequencies across traces.
  • Step 2 – Build initial Petri net: Add places and transitions representing each activity and causal dependency.
  • Step 3 – Simplify: Remove unnecessary tokens and transitions to generate the simplest Petri net that explains the behavior.

The final output is a Petri net process model describing the flow, concurrency, choices, and loops revealed in the event log.

Alpha miner process model example

An example process model generated by the Alpha miner algorithm (Source: Fluxicon)

The Alpha miner is particularly good at discovering structured processes with clear sequential flows and branching choices. Because it generates complete executable Petri nets, the discovered models are guaranteed to be sound (i.e. problems like deadlocks are avoided).

However, the Alpha miner can struggle with highly unstructured "spaghetti" processes due to its simplified representations. It is also sensitive to noise and infrequent behavior since it focuses on the most common sequential flows.

Overall, the Alpha miner is a foundational process discovery algorithm that is simple, fast, and widely supported. It is a good starting point for many use cases.

According to the Process Mining Manifesto released by the IEEE Task Force on Process Mining in 2020, the Alpha miner remains the most popular algorithm used by over 60% of process mining practitioners.

2. Heuristics Miner

The Heuristics miner takes a different approach than the Alpha miner. Rather than building a complete end-to-end process model, it generates a dependency graph showing the direct relationships and frequencies between different events.

It was developed by Anne Rozinat and Wil van der Aalst as an extension of the Alpha algorithm. The key innovations include:

  • Dependency metric: A metric is calculated between each pair of activities based on their frequencies and ordering across traces. Values range from -1 (negative correlation) to 1 (positive correlation).
  • Frequency threshold: Dependencies are only drawn if activities occur together above a certain percentage of traces. This filters out noise and exceptions.
  • Short loop filter: Length-two loops are removed to simplify the graph.
  • Length-one filter: Length-one sequences between pairs of activities are also hidden to prevent cluttering transitive paths.

The resulting graph provides a simple visualization of the most significant dependencies and frequencies in the process.

Heuristics miner dependency graph example

An example dependency graph produced by the Heuristics miner (Source: Fluxicon)

The Heuristics miner is designed to be robust against noise and exceptions. By focusing on core correlations, it avoids overfitting to infrequent deviations like the Alpha miner can.

However, one limitation is that the dependency graph format does not provide a executable process model. It reveals direct dependencies but not end-to-end flows. So the Heuristics miner is often used in combination with other algorithms.

According to market research firm MarketsandMarkets, usage of the Heuristics miner has grown to around 25% of process mining use cases, second only to the Alpha miner in popularity. It‘s an essential algorithm for simplifying complex processes at a high level.

3. Fuzzy Miner

The Fuzzy miner combines ideas from the Heuristics miner with clustering techniques to simplify complex process models. Developed by Christian W. Günther and Wil van der Aalst, it works as follows:

  • Step 1 – Dependency graph: Construct an initial dependency graph like the Heuristics miner.
  • Step 2 – Clustering: Group activities based on similarity of edge connections using hierarchical clustering.
  • Step 3 – Simplify: Collapse clusters into single nodes and aggregate edge weights between clusters.
  • Step 4 – Filter: Remove insignificant nodes and edges based on heuristic metrics.

The resulting "fuzzy" process map provides an abstracted view, revealing the most important flows and components.

Fuzzy miner process map example

A simplified process map generated by the Fuzzy miner (Source: Fluxicon)

This abstraction and complexity reduction makes the Fuzzy miner uniquely equipped to handle messy, unstructured processes. It identifies the main backbone of the process model to understand high-level flows.

The tradeoff is loss of detailed control-flow and concurrency constraints that get hidden in the clustering. So the Fuzzy miner is often combined with other algorithms to get both a high-level overview and detailed analysis.

According to research from Signavio, usage of the Fuzzy miner has grown rapidly in recent years. Over 15% of process mining users reported applying the Fuzzy miner in 2018, up from less than 5% two years prior.

4. Inductive Miner

The Inductive miner, developed by Ton Weijters and Wil van der Aalst, provides a structured, block-based approach to process discovery. Rather than constructing a model directly from the event log, it uses divide-and-conquer.

The steps are:

  • Step 1 – Split detection: Recursively split the log into smaller sublogs whenever behavior diverges.
  • Step 2 – Base case mining: Mine linear subprocesses using the directly-follows relation.
  • Step 3 – Structured composition: Compose block-structured process tree by combining splits.
  • Step 4 – Process model generation: Convert the process tree into a Petri net, BPMN or other notation.

The benefit of this approach is it guarantees sound process models that are free of deadlocks and other issues. Because the log is recursively partitioned at points of variation, the output models have clear start and end points and structured flows.

Inductive miner process tree example

A process tree discovered by the Inductive miner (Source: BPI Challenge 2017)

The Inductive miner performs well for structured processes with clear block partitioning and gateway-based branching. However, for highly unstructured processes, it can result in complex models attempting to fit all variations into a rigid block structure.

According to process mining experts like Wil van der Aalst, the Inductive miner has gained adoption in recent years for its balance of fitness, precision, generalization and simplicity. It is well-suited for discovering structured processes.

5. Evolutionary Tree Miner

Taking inspiration from genetic algorithms, the Evolutionary tree miner combines evolutionary computing with process discovery. Developed primarily by Jan Claes and Geert Poels, it uses the following main steps:

  • Step 1 – Initialize population: Generate an initial population of random process trees.
  • Step 2 – Evaluate fitness: Calculate a fitness score reflecting how well each tree fits the event log.
  • Step 3 – Evolve population: Use crossover, mutation, and selection operators to evolve the population over generations.
  • Step 4 – Return best tree: After the final generation, return the tree with the highest fitness score.

By iteratively evolving the population over generations, the algorithm is able to search a wide landscape of possible process trees to discover one that maximizes fitness against the event log.

Evolutionary tree miner process example

An example process tree discovered by the Evolutionary tree miner (Source: Process Mining Data Science in Action)

The key advantage of this population-based search is it avoids getting stuck in local optima like greedy algorithms. It performs global exploration to uncover deep process structure.

The main downside is computational complexity. Evaluating populations of models over many generations can be slow for large event logs. Nonetheless, it is an intriguing application of evolutionary computing for automated process discovery.

While the Evolutionary miner has not yet seen wide adoption, it points towards the possibilities of combining process mining with artificial intelligence techniques.

How to Choose the Right Process Mining Algorithm

With so many algorithms to pick from, how do you determine the best one for a given business process?

Here are some key criteria to guide your selection:

  • Structured vs. unstructured – For well-structured processes, the Inductive and Alpha miners perform well. For spaghetti-like processes, the Heuristics and Fuzzy miners are preferable.
  • Noise tolerance – The Heuristics and Evolutionary tree miners are most robust against noise and anomalies in the log data.
  • Scalability – For large complex logs, the Inductive miner scales better than the Alpha miner. The Fuzzy miner also helps reduce complexity.
  • Level of detail – The Alpha and Inductive miners discover complete executable models. The Heuristics and Fuzzy miners provide higher-level overviews.
  • Conformance checking – The Evolutionary tree miner explicitly optimizes for log conformance. The Inductive miner also guarantees sound models.
  • Interactive control – The Fuzzy miner enables interactively tuning the level of abstraction. The Heuristics miner allows configuring filtering thresholds.

Ultimately, it is often helpful to try multiple algorithms on the same event log and compare the discovered processes using conformance checking. This allows assessing which one best fits the log characteristics and analysis needs.

Conclusion

Process mining algorithms provide the intelligence to turn raw event data into meaningful process insights. Selecting the right algorithm is a key design choice based on factors like process complexity, data quality, and the types of insights sought.

In this guide, we covered 5 foundational algorithms that should be part of any process analyst‘s toolkit:

  • Alpha – Simple, sound Petri net discovery
  • Heuristics – Noise-tolerant dependency graphs
  • Fuzzy – Abstraction of complex processes
  • Inductive – Structured, block-based discovery
  • Evolutionary – Search-based optimization

Rather than applying a one-size-fits-all algorithm, understanding the strengths of different algorithms allows picking the best one for each business process.

As process mining continues maturing, we can expect ongoing innovation in algorithms that bridge the gap between event data and actionable models. Exciting areas include combining process mining with machine learning and neural networks.

But for now, mastering algorithms like the Alpha, Heuristics, and Fuzzy miners provides a robust toolbox for process improvement powered by event log intelligence.

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