Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development. The course uses many examples using real-life event logs to illustrate the concepts and algorithms. 1 Efﬁcient Selection of Process Mining Algorithms Jianmin Wang, Raymond K. Wong, Jianwei Ding, Qinlong Guo and Lijie Wen Abstract—While many process mining. There are many process mining algorithms and representations, making it difficult to choose which algorithm to use or compare results. Process mining is essentially a. /faculteit technologie management. 3. Process Mining • Short Recap • Types of Process Mining Algorithms • Common Constructs • Input Format • α-algorithm. Tags: Algorithms, Apriori, Bayesian, Boosting, C4.5, CART, Data Mining, Explained, K-means, K-nearest neighbors, Page Rank, Support Vector Machines, Top 10 Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why .

**By Raymond Li.**

Today, I’m going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper.

Once you know what they are, how they work, what they do and where you can find them, my hope is you’ll have this blog post as a springboard to learn even more about data mining.

What are we waiting for? Let’s get started!

Here are the algorithms:

- 1. C4.5
- 2. k-means
- 3,
**process mining algorithms**. Support vector machines - 4. Apriori
- 5. EM
- 6. PageRank
- 7. AdaBoost
- 8. kNN
- 9. Naive Bayes
- 10. CART

We also provide interesting resources at the end.

**1. C4.5**

**What does it do? **C4.5 constructs a classifier in the form of a decision tree. In order to do this, C4.5 is given a set of data representing things that are already classified.

**Wait, what’s a classifier? **A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to.

**What’s an example of this? **Sure, suppose a dataset contains a bunch of patients. We know various things about each patient like age, pulse, blood pressure, VO_{2}max, family history, etc. These are called attributes.

Now:

Given these attributes, we want to predict whether the patient will get cancer. The patient can fall into 1 of 2 classes: will get cancer or won’t get cancer. C4.5 is told the *process mining algorithms* for each patient.

And here’s the deal:

Using a set of patient attributes and the patient’s corresponding class, C4.5 constructs a decision tree that can predict the class for new patients based on their attributes.

**Cool, so what’s a decision tree?** Decision tree learning creates something similar to a flowchart to classify new data. Using the same patient example, one particular path in the flowchart could be:

- Patient has a history of cancer
- Patient is expressing a gene highly correlated with cancer patients
- Patient has tumors
- Patient’s tumor size is greater than 5cm

The bottom line is:

At each point in the flowchart is a question about the value is branch mining some attribute, and depending on those values, he or she gets classified. You can find lots of examples of decision trees.

**Is this supervised or unsupervised?** This is supervised learning, since the training dataset is labeled with classes. Using the patient example, *Process mining algorithms* doesn’t learn on its own that a patient will get cancer or won’t get cancer. We told it first, it generated a decision tree, and now it uses the decision tree to classify.

**You might be wondering how C4.5 is different than other decision tree systems?**

- First, C4.5 uses information gain when generating the decision tree.
- Second, although other systems also incorporate pruning, C4.5 uses a single-pass pruning process to mitigate over-fitting. Pruning results in many improvements.
- Third, C4.5 can work with both continuous and discrete data,
*process mining algorithms*. My understanding is it does this by specifying ranges or thresholds for continuous data thus turning continuous data into discrete data. - Finally, incomplete data is dealt with in its own ways.

**Why use C4.5?** Arguably, the best selling point of decision *process mining algorithms* is their ease of interpretation and explanation. They are also quite fast, quite popular and the output is human readable.

**Where is it used? **A popular open-source Java implementation can be found over at OpenTox. Orange, *process mining algorithms*, an open-source data visualization and analysis tool for data mining, implements C4.5 in their decision tree classifier.

Classifiers are great, but make sure to checkout the next algorithm about clustering…

**2. k-means**

**What does it do? **k-means creates *k* groups from a set of objects so that the members of a group are more similar. It’s a popular cluster analysis technique for exploring a dataset.

**Hang on, what’s cluster analysis?** Cluster analysis is a family of algorithms designed to *process mining algorithms* groups such that the group members are more similar versus non-group members. Clusters and groups are synonymous in the world of cluster analysis.

**Is there an example of this? **Definitely, suppose we have a dataset of patients. In cluster analysis, these would be called observations. We know various things about each patient like age, pulse, blood pressure, VO_{2}max, cholesterol, etc. This is a vector representing the patient.

Look:

You can basically think of a vector as a list of numbers we know about the patient. This list can also be interpreted as coordinates in multi-dimensional space. Pulse can be one dimension, blood pressure another dimension and so forth.

You might be wondering:

Given this set of vectors, how do we cluster together patients that have similar age, pulse, blood pressure, etc?

Want to know *process mining algorithms* best part?

You tell k-means how many clusters you want. K-means takes care of the rest.

**How does k-means take care of the rest?** k-means has lots of variations **process mining algorithms** optimize for certain types of data.

At a high level, they all do something like this:

- k-means picks points in multi-dimensional space to represent each of the k clusters. These are called centroids.
- Every patient will be closest to 1 of these k centroids. They hopefully won’t all be closest to the same one, so they’ll form a cluster around their nearest centroid.
- What we have are k clusters, and each patient is now a member of a cluster.
- k-means then finds the center for each of the k clusters based on its cluster members (yep, using the patient vectors!).
- This center becomes the new centroid for the cluster.
- Since the centroid is in a different place now, patients might now be closer to other centroids. In other words, they may change cluster membership.
- Steps 2-6 are repeated until the centroids no longer change, and the cluster memberships stabilize. This is called convergence.

**Is this supervised or unsupervised?** It depends, but most would classify k-means as unsupervised. Other than specifying the number of clusters, k-means “learns” the clusters on its own without any information about which cluster an observation belongs to. k-means can be semi-supervised.

**Why use k-means?** I don’t think many saint mining have an issue with this:

The key selling point of k-means is its simplicity. Its simplicity means it’s generally faster and more efficient than other algorithms, especially over large datasets.

It gets better:

k-means can be used to pre-cluster a massive dataset followed by a more expensive cluster analysis on the sub-clusters. k-means can also be used to rapidly “play” with k and explore whether there are **process mining algorithms** patterns or relationships in the dataset.

It’s not all smooth sailing:

Two key weaknesses of k-means are its sensitivity to outliers, and its sensitivity to the initial choice of centroids. One final thing to keep in mind is k-means is designed to operate on continuous data — you’ll need to do some tricks to get it to work on discrete data.

**Where is it used? **A ton of implementations for k-means clustering are available online:

If decision trees and clustering didn’t impress you, *process mining algorithms*, you’re going to love the next algorithm.

**3. Support vector machines**

**What does it do? **Support vector machine (SVM) learns a hyperplane to classify data into 2 classes. At a high-level, SVM performs a similar task like C4.5 except SVM doesn’t use decision trees at all.

**Whoa, a hyper-what? **A hyperplane is a function like the equation for a line, *y = mx + b*. In fact, for a simple classification task with just 2 features, **process mining algorithms**, the hyperplane can be a line.

As it turns out…

SVM can perform a trick to project your data into higher dimensions. Once projected into higher dimensions…

…SVM figures out the best hyperplane which separates your data into the 2 classes.

**Do you have an example? **Absolutely, the simplest example I found starts with a bunch of red and blue balls on a table. If the balls aren’t too mixed together, you could take a stick and without moving the balls, **process mining algorithms**, separate them with the stick.

You see:

When cloud mining ru new ball is added on the table, by knowing which side of the stick the ball is on, you can predict its color.

**What do the balls, table and stick represent?** The balls represent data points, and the red and blue color represent 2 classes. The stick represents the simplest hyperplane which is a line.

And the coolest part?

SVM figures out the function for the hyperplane.

**What if things get more complicated? **Right, they frequently do. If the balls are mixed together, a straight stick won’t work.

Here’s the work-around:

Quickly lift up the table throwing the balls in the air. While the balls are in the air and thrown up in just the right way, you use a large sheet of paper to divide the balls in the air.

You might be wondering if this is cheating:

Nope, lifting up the table is the equivalent of mapping your data into higher dimensions. In this case, we go from the 2 dimensional table surface to the 3 dimensional balls in the air.

**How does SVM do this?** By using a kernel we have a nice way to operate in higher dimensions, *process mining algorithms*. The large sheet of paper is still called a hyperplane, but it is now a function for a plane rather than a line. Note from Yuval that once we’re in 3 dimensions, the hyperplane must be a plane rather than a line.

I found this visualization super helpful:

Reddit also has 2 great threads on this in the ELI5 and ML subreddits.

**How do balls on a table or in the process mining algorithms map to real-life data?** A ball on a table has a location that we can specify using coordinates. For example, a ball could be 20cm from the left edge and 50cm from the bottom edge. Another way to describe the ball is as (x, y) coordinates or (20, 50). x and y are 2 dimensions of the ball.

Here’s the deal:

If we had a patient dataset, each patient could be described by various measurements like pulse, cholesterol level, blood pressure, etc. Each of these measurements is a dimension.

The bottom line is:

SVM does its thing, maps them into a higher dimension and then finds the hyperplane to separate the classes.

**Margins are often associated with SVM? What are they?** The margin is the distance between the hyperplane and the 2 closest data points from each respective class. In the ball and table example, the distance between the stick and the closest red and blue ball is the margin.

The key is:

SVM attempts to maximize the margin, so that the hyperplane is just as far away from red ball as the blue ball. In this way, it decreases the chance of misclassification.

**Where does SVM get its name from?** Using the ball and table example, the hyperplane is equidistant from a red ball and **process mining algorithms** blue ball. These balls or data points are called support vectors, because yacoin mining pools support the hyperplane.

**Is this supervised or unsupervised?** This is a supervised learning, **process mining algorithms**, since a dataset is used to first teach the SVM about the classes. Only then is the SVM capable of classifying new data.

**Why use SVM?** SVM along with C4.5 are generally the 2 classifiers to try first. No classifier will be the best in all cases due to the No Free Lunch Theorem. In addition, kernel selection and interpretability are some weaknesses.

**Where is it used? **There are many implementations of SVM. A few of the popular ones are scikit-learn, MATLAB and of course libsvm.

The next algorithm is one of my favorites…

Pages: 1 23

### A Framework for the Analysis of Process Mining Algorithms | Philip Weber - Academia.edu

Tags: Algorithms, Apriori, Bayesian, Boosting, C4.5, CART, Data Mining, Explained, K-means, K-nearest neighbors, Page Rank, Support Vector Machines, Top 10 Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why . A Process Mining Approach in Software Development and Testing Process: ProM Framework and Process Mining Algorithms, SOFTWARE DEVELOPMENT AND TESTING PROCESS. PROCESS MINING OF EVENT PROCESS MINING OF EVENT LOGS IN AUDITING: OPPORTUNITIES AND CHALLENGES accompanied by sophisticated search and analytic algorithms. process models) from event logs and exploit it for further analysis. Event logs record the start and/or completion of various tasks in a process instance. The process models extracted from such logs using process mining algorithms are called "mined" models, and they describe the actual behavior of a business process. A Process Mining Technique Using Pattern Recognition. The time needed by our algorithm to process mine and existing process mining techniques focus on. www.processmining.org.### www.processmining.org. 1 Efﬁcient Selection of Process Mining Algorithms Jianmin Wang, Raymond K. Wong, Jianwei Ding, Qinlong Guo and Lijie Wen Abstract—While many process mining. Process mining is a process management technique that allows for the analysis of business processes based on event logs. During process mining, specialized data-mining algorithms are applied to event log datasets in order to identify trends, patterns and details contained in event logs recorded by an information system. Process mining aims to .

**About this course: **Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as "data science in action". The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains. This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments. The course covers the three main types of process mining. 1. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log. 2. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa. 3. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases. Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development. The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field. After taking this course you should: - have a good understanding of Business Process Intelligence techniques (in particular process mining), - understand the role of Big Data in today’s society, - be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification, - be able to apply basic process discovery techniques to learn a process model from an event log (both manually and using tools), - be able to apply basic conformance checking techniques to compare event logs and process models (both manually and using tools), - be able to extend a process model with information extracted from the event log (e.g., show bottlenecks), - have a good understanding of the data needed to start a process mining project, - be able to characterize the questions that can be answered based on such event data, - explain how process mining can also be used for operational support (prediction and recommendation), and - be able to conduct process mining projects in a structured manner.

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