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 …

Learning from data streams is a research area of increasing importance. Nowadays, several
stream learning algorithms have been developed. Most of them learn decision models that …

This paper presents and analyzes an incremental system for clustering streaming time
series. The Online Divisive-Agglomerative Clustering (ODAC) system continuously …

Data stream mining is a fast growing research topic due to the ubiquity of data in several real-
world problems. Given their ephemeral nature, data stream sources are expected to …

Data Warehousing and Mining (DWM) is the science of managing and analyzing large
datasets and discovering novel patterns and in recent years has emerged as a particularly …

The most challenging applications of knowledge discovery involve dynamic environments
where data continuous flow at high-speed and exhibit non-stationary properties. In this …

Decision rules, which can provide good interpretability and flexibility for data mining tasks,
have received very little attention in the stream mining community so far. In this work we …

Designing efficient algorithms for mining massive high-speed data streams has become one
of the contemporary challenges for the machine learning community. Such models must …

Decision rules are one of the most expressive and interpretable models for machine
learning. In this article, we present Adaptive Model Rules (AMRules), the first stream rule …

Incremental learning techniques have been used extensively to address the data stream
classification problem. The most important issue is to maintain a balance between accuracy …