Explosive growth in geospatial data and the emergence of new spatial technologies
emphasize the need for automated discovery of spatial knowledge. Spatial data mining is …

Disaster management is a crucial and urgent research issue. Emergency communication
networks (ECNs) provide fundamental functions for disaster management, because …

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 …

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 …

Outlier detection is a primary step in many data-mining applications. We present several
methods for outlier detection, while distinguishing between univariate vs. multivariate …

Mining outliers in database is to find exceptional objects that deviate from the rest of the data
set. Besides classical outlier analysis algorithms, recent studies have focused on mining …

A spatial outlier is a spatially referenced object whose non-spatial attribute values are
significantly different from the values of its neighborhood. Identification of spatial outliers can …

Analyzing the quality of data prior to constructing data mining models is emerging as an
important issue. Algorithms for identifying noise in a given data set can provide a good …

Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and
Diagnosis discusses various white-and black-box approaches to fault diagnosis in condition …

The advent and progressive deployment of the so-called Smart Grid has unleashed a
profitable portfolio of new possibilities for an efficient management of the low-voltage …