Clustering for Machine Learning
Clustering is a technique that can be used to explore patterns and structures within data. It has wide application in business analytics and in machine learning.
What you’ll learn
- When to use clustering techniques in general, and which algorithms are best for solving specific business problems.
- How to use clustering for missing value imputation and anomaly detection.
- How to use clustering for segmentation/customer profiling.
Meet the Speaker
Ilknur Kaynar-Kabul is a Senior Manager in the SAS Advanced Analytics division, where she leads the SAS R&D team that focuses on machine learning algorithms. The team is responsible for researching and implementing new data mining and machine learning algorithms that can solve complex big data problems in the high-performance analytics environment. Her research interests include clustering, deep learning, feature extraction methods, nearest neighborhood search, ensemble models and visualization techniques. Prior to joining SAS, Kaynar- Kabul worked on medical image analysis and visualization techniques at The University of North Carolina at Chapel Hill and Kitware. She is the co-inventor of the aligned box criterion (ABC) method for finding a number of clusters and an automated clustering system for market segmentation. She has a PhD in computer science from UNC Chapel Hill.