The main purpose of data mining and analytics is to find novel, potentially useful patterns
that can be utilized in real-world applications to derive beneficial knowledge. For identifying …

Itemset mining is an important subfield of data mining, which consists of discovering
interesting and useful patterns in transaction databases. The traditional task of frequent …

A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …

The significant growth of interconnected Internet‐of‐Things (IoT) devices, the use of social
networks, along with the evolution of technology in different domains, lead to a rise in the …

Most streaming decision models evolve continuously over time, run in resource-aware
environments, and detect and react to changes in the environment generating data. One …

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 …

In the last two decades, machine learning research and practice has focused on batch
learning, usually with small datasets. Nowadays there are applications in which the data are …

Frequent pattern mining has been a focused theme in data mining research for over a
decade. Abundant literature has been dedicated to this research and tremendous progress …

A motion object extraction algorithm based on the active contour model is proposed. Firstly,
moving areas involving shadows are segmented with the classical background difference …

Stream processing is a novel distributed computing paradigm that supports the gathering,
processing, and analysis of high-volume, heterogeneous, continuous data streams, to …