Most data mining approaches assume that the data can be provided from a single source. If
data was produced from many physically distributed locations like Wal-Mart, these methods …

Data mining (DM) is increasingly used in the analysis of data generated in life sciences,
including biological data produced in several disciplines such as genomics and proteomics …

Effective Big Data Mining requires scalable and efficient solutions that are also accessible to
users of all levels of expertise. Despite this, many current efforts to provide effective …

Dengue infection is vital disease caused by dengue germ, which extent in body of human by
female mosquito [1]. With indications of headache, retro orbital pain, joint-pain, muscular …

The enormous growth of data in a variety of applications has increased the need for high
performance data mining based on distributed environments. However, standard data …

The DataMiningGrid system has been designed to meet the requirements of modern and
distributed data mining scenarios. Based on the Globus Toolkit and other open technology …

Data mining tools may be computationally demanding, so there is an increasing interest on
parallel computing strategies to improve their performance. The popularization of Graphics …

Current business processes often use data from several sources. Data is characterized to be
heterogeneous, incomplete and usually involves a huge amount of records. This implies that …

Data mining tools may be computationally demanding, which leads to an increasing interest
on parallel computing strategies in order to improve their performance. While multi-core …

Massive parallelism is required for an efficient solution to data mining tasks, considering the
proliferation of data and the need for high computational effort. The DisDaMin (Distributed …