Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

Concept drift primarily refers to an online supervised learning scenario when the relation
between the input data and the target variable changes over time. Assuming a general …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

In this paper we focus on the anomaly detection problem for dynamic data streams through
the lens of random cut forests. We investigate a robust random cut data structure that can be …

In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

Abstract Concept drift is a phenomenon that commonly happened in data streams and need
to be detected, because it means the statistical properties of a target variable, which the …

Data Mining in non-stationary data streams is gaining more attentionrecently, especially in
the context of Internet of Things and Big Data. It is a highly challenging task, since the …

Abstract Concept drift may lead to a sharp downturn in the performance of streaming in data-
based algorithms, caused by unforeseeable changes in the underlying distribution of data …

Streaming data are increasingly present in real-world applications such as sensor
measurements, satellite data feed, stock market, and financial data. The main characteristics …

In recent years there has been a noticeable shift in attention from those who use agile
software development toward lean software development, often labelled as a shift “from …