Data Clustering: 50 Years Beyond K-means

Data Clustering: 50 Years Beyond K-means

2008 | Anil K. Jain
The chapter "Data Clustering: 50 Years Beyond K-means" by Anil K. Jain from Michigan State University discusses the fundamental importance of clustering in organizing and understanding data. Clustering is a method for grouping objects based on their intrinsic characteristics, without using prior category labels, making it unsupervised learning. The development of clustering methods has been interdisciplinary, involving contributions from various fields such as taxonomy, social sciences, and computer science. K-means, a well-known and simple clustering algorithm, has been independently discovered multiple times and remains widely used. The chapter highlights the vast literature on clustering, emphasizing its significance in machine learning, data mining, and pattern recognition. It also addresses the challenges in defining and identifying clusters, the choice of algorithms, distance metrics, and validation criteria. The talk will cover background, major challenges, key issues, well-known methods, and emerging research directions, including semi-supervised clustering, ensemble clustering, learning distance metrics from side information, and simultaneous feature selection and clustering.The chapter "Data Clustering: 50 Years Beyond K-means" by Anil K. Jain from Michigan State University discusses the fundamental importance of clustering in organizing and understanding data. Clustering is a method for grouping objects based on their intrinsic characteristics, without using prior category labels, making it unsupervised learning. The development of clustering methods has been interdisciplinary, involving contributions from various fields such as taxonomy, social sciences, and computer science. K-means, a well-known and simple clustering algorithm, has been independently discovered multiple times and remains widely used. The chapter highlights the vast literature on clustering, emphasizing its significance in machine learning, data mining, and pattern recognition. It also addresses the challenges in defining and identifying clusters, the choice of algorithms, distance metrics, and validation criteria. The talk will cover background, major challenges, key issues, well-known methods, and emerging research directions, including semi-supervised clustering, ensemble clustering, learning distance metrics from side information, and simultaneous feature selection and clustering.
Reach us at info@study.space