For instance, Parallel and Distributed Computing for Data Mining is a book that delves into the fundamentals and applications of these topics, with examples and case studies from different domains.

There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of sequences, or data series. Examples of ...

Traditional methods for data mining typically make the assumption that the data is centralized, memory-resident, and static. This assumption is no longer tenable. Such methods waste computational and ...

Keywords: parallel computing, distributed computing, deep learning, data mining, machine learning Important note: All contributions to this Research Topic must be within the scope of the section and ...

Distributed data mining (DDM) refers to the scenario where the data is stored and processed across multiple nodes or sites, such as in a network, a grid, or a cluster.

Moreover, distributed data mining algorithms can optimize the exchange of data needed to develop global knowledge models based on concurrent mining of remote datasets. By exploiting this open ...

The project explores the advantages of Parallel and Distributed Processing (PDP) techniques to improve efficiency in data analysis for large datasets. By utilizing the NYC Taxi Trip Dataset ...

This transition from a sequential to a parallel and distributed environment poses unique challenges. It is not just about sorting data efficiently but also about how to divide the entire task into ...

Recent advances in digital pathology technology have led to significant improvements in terms of both the quality and resolution of the resulting images, which now often exceed several gigabytes each.