" Big Data" as a term has been among the biggest trends of the last three years, leading to
an upsurge of research, as well as industry and government applications. Data is deemed a …

The recent advances in hardware and software have enabled the capture of different
measurements of data in a wide range of fields. These measurements are generated …

This chapter attempts a concise introduction to data mining and knowledge discovery. First,
we introduce the necessary nomenclature and definitions, discuss the background of the …

Clustering ensembles have emerged as a powerful method for improving both the
robustness as well as the stability of unsupervised classification solutions. However, finding …

This paper explores the possibility of using multiplicative random projection matrices for
privacy preserving distributed data mining. It specifically considers the problem of computing …

Clustering ensembles have emerged as a powerful method for improving both the
robustness and the stability of unsupervised classification solutions. However, finding a …

With growing concerns regarding data privacy and rapid increase in data volume, Federated
Learning (FL) has become an important learning paradigm. However, jointly learning a deep …

In this paper, we study the problem of constructing private classifiers using decision trees,
within the framework of differential privacy. We first construct privacy-preserving ID3 decision …

Bioinformatics research is characterized by voluminous and incremental datasets and
complex data analytics methods. The machine learning methods used in bioinformatics are …

Privacy is becoming an increasingly important issue in many data-mining applications. This
has triggered the development of many privacy-preserving data-mining techniques. A large …