This paper presents the top 10 data mining algorithms identified by the IEEE International
Conference on Data Mining (ICDM) in December 2006: C4. 5, k-Means, SVM, Apriori, EM …

Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

Abstract We propose Prototypical Networks for the problem of few-shot classification, where
a classifier must generalize to new classes not seen in the training set, given only a small …

A key learning scenario in large-scale applications is that of federated learning, where a
centralized model is trained based on data originating from a large number of clients. We …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

Most learning approaches treat dimensionality reduction (DR) and clustering separately (ie,
sequentially), but recent research has shown that optimizing the two tasks jointly can …

Introduction to Data Mining Page 1 CHAPTER 14 ■ ■ ■ 369 Introduction to Data Mining In
this chapter, we’ll explore the incredibly powerful tools included with SQL Server Analysis …

This book addresses some theoretical aspects of semisupervised learning (SSL). The book
is organized as a collection of different contributions of authors who are experts on this topic …