Data Science & Analytics @ LIRIS, KU Leuven
This website contains information about the Data Mining, Data Science and Analytics Research conducted in the research team chaired by prof. dr. Bart Baesens and prof. dr. Seppe vanden Broucke at KU Leuven (Belgium).
Current topics of interest include:
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“Data Science Briefings” is the essential guide for data scientists and data-driven practitioners to keep up to date with the latest news and trends on data mining and analytics.
What to expect:
- An e-mail newsletter every two weeks (or so)
- Containing an overview of interesting tools, techniques, trends and news on data mining and analytics
- The essential guide for any data scientist or data-driven practitioner
- Highlights about ongoing projects and research here at DataMiningApps
- Feature articles written by our researchers
Great resources
Our team has authored an impressive amount of books on topics such as fraud analytics, big data, machine learning, credit risk, programming, and more.
In addition, we're glad to offer our researchers' PhD theses available for download for free on this site as well.
Marketing Analytics: All about the customer
Optimizing the interactions between you and your customers.
Our research focuses on:
- Customer churn (retention) modeling
- Customer segmentation and profiling
- Customer value (CLV) modeling
- Recommender systems
- Engagement modeling
- Profit-driven modeling and model evaluation
- Incorporating social network data
- Novel techniques to optimize lift and to deal with class imbalance
Credit Analytics: New approaches for better credit risk modelling
Given the on-going turmoil on credit markets, a critical re-assessment of current capital and
credit risk modelling approaches is more than ever needed.
Our research focuses on:
- Novel credit risk modelling techniques
- Applying survival analysis techniques to credit risk
- Studying the impact of data quality
Fraud Analytics: Gotcha or Gotch'all?
Fraud has become a billion-dollar business that is increasing every year. The emphasis of our research shifts the focus from fraud detection towards fraud prevention.
Our research focuses on:
- Detecting fraud before it is even committed
- Incorporating networked data to uncover fraudulent patterns
- Detect in real-time when certain processes show characteristics of irregular activities
Process Analytics: Including flows, journeys, and logs
Following flows of data requires appropriate techniques, whether it is a process, a customer journey, or operational logs.
Our research focuses on:
- Industry applications of process mining: telecom industry, banking industry, government
- Applying process mining related techniques to marketing analytics and
customer-centric scenarios such as customer journey mapping - New process quality evaluation metrics for robust conformance checking
- Process clustering techniques to untangle and explain complex models
- Developing predictive analytics techniques towards process-oriented contexts,
e.g. lead-time prediction, executor recommendation systems
Human Resource Analytics: Understand employee behavior
If analytics can be used to study customer behavior, it can also be beneficial to better understand your employee behavior.
Our research focuses on:
- Measuring employee performance and engagement
- Analyzing employee churn and turnover
- Studying workforce collaboration patterns
- Predicting employee absenteeism
- Modeling employee lifetime value
Practical Web Scraping for Data Science -- Our book on web scraping for data scientists!
Check webscrapingfordatascience.com for more information!
Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data. Check pdbmbook.com for more information!
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