Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …

Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

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 …

Multivariate time series data come as a collection of time series describing different aspects
of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a …

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 …

Time series clustering is an active research area with applications in a wide range of fields.
One key component in cluster analysis is determining a proper dissimilarity measure …

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 …

Networks (or graphs) appear as dominant structures in diverse domains, including
sociology, biology, neuroscience and computer science. In most of the aforementioned …

In almost every scientific field, measurements are performed over time. These observations
lead to a collection of organized data called time series. The purpose of time-series data …

Time series is an important class of temporal data objects and it can be easily obtained from
scientific and financial applications. A time series is a collection of observations made …