With the continued rise of Process Mining technology, more and more people are interested in learning more about this new area of data science and the details of how it works.
In this article, I will discuss the different process mining algorithms that can be applied to analyze and transform data into a usable process map.
What is a Process Mining Algorithm?
Process mining algorithms are used to discover process models from event logs, which are detailed records of the activities that take place within a system or process. Process mining algorithms can be used to discover the underlying structure of a process, identify inefficiencies or bottlenecks, and monitor and improve the performance of the process over time. Some of the most used process mining algorithms include the Inductive Miner, the Alpha Miner, and the Heuristic Miner.
Let’s look at the most common ones…
The Alpha Miner algorithm was the first algorithm invented for process discovery. It is a constructive approach, since it constructs only one model, and is based on the directly follows graph and the notion of so-called footprints, the prints that a process leaves in the log, such as causality, concurrency, and exclusion. The Alpha Miner algorithm is particularly well-suited for discovering processes in which concurrent activities are common, such as in the case of distributed systems or processes involving multiple stakeholders. The Alpha algorithm cannot work on noisy data.
Like the Alpha Miner algorithm, the Heuristic Miner uses a constructive approach, starting the directly follows graph. However, the Heuristic Miner algorithm applies filtering to reduce noise, thus allowing the discovery of models from noisy event logs. The resulting models are flowcharts that are typically less precise than the models discovered by the Alpha Miner but are known to be robust to noisy and incomplete data.
Fuzzy miner is a process discovery algorithm that works similarly to a GPS software. It tries to discover models depending on the resolution that the user desires. If the user zooms in, the model will include more details. When the user zooms out, the model is clustered and becomes fuzzier (which gives the algorithm its name). The algorithm is based on activity filtering, clustering, and applies more sophisticated approaches than the Alpha and the Heuristic miners. Fuzzy miner is often used in conjunction with other process mining algorithms, such as heuristics miner and alpha miner, to provide a more complete picture of an organization’s processes.
The Inductive Miner algorithm uses a divide-and-conquer approach, starting with the entire log, and splitting it into sub-logs based on a sequence of so-called cuts. It is a popular algorithm for process mining because it guarantees that the resulting models are fitting and sound. Moreover, its infrequent version, the inductive miner – infrequent can handle noisy data, and produce high-quality process models that are easy to understand and interpret.
A genetic miner uses evolutionary algorithms to search for patterns or relationships within large sets of data. Genetic algorithms are inspired by the principles of natural selection and genetics and are commonly used for other data mining and machine learning tasks. The genetic miner uses a set of rules to generate a population of potential solutions, which are then evaluated and modified through a process of selection, crossover, and mutation. The goal is to find the best process model that would fit the data. Specifically, the algorithm stops when a fitting, precise and simple model is uncovered.
With process mining being adopted at such a rapid pace, the technology itself continues to advance at an incredible rate. With this, the mining algorithms will also mature as Process Mining providers explore new ways to best represent the process data and maximize a business’s ability to gain insight into how their processes actually work.
Process Mining Simplified