Data Mining is often required to be performed among a number of groups of sites, where the precondition is that no privacy of any site should be leaked out to other sites. In this paper, a hierarchical infrastructure is proposed for large-scale distributed Privacy Preserving Data Mining (PPDM) utilizing a synergy between P2P and Grid. The proposed architecture is characterized with (1) its abil...

Support Vector Machines (SVM) are powerful classification and regression tools. They have been widely studied by many scholars and applied in many kinds of practical fields. But their compute and storage requirements increase rapidly with the number of training vectors, putting many problems of practical interest out of their reach. For applying SVM to large scale data mining, parallel SVM are ...

Distributed Association Rule Mining (DARM) is the task for generating the globally strong association rules from the global frequent itemsets in a distributed environment. The intelligent agent based model, to address scalable mining over large scale distributed data, is a popular approach to constructing Distributed Data Mining (DDM) systems and is characterized by a variety of agents coordina...

Nowadays, there is an increasing demand tomonitor, analyze, and control large scale distributed systems. Events detected during monitoring are temporally correlated, which is helpful to resource allocation, job scheduling, and failure prediction. To discover the correlations among detected events, many existing approaches concentrate detected events into an event database and perform data minin...

The uncontrolled spread of illegal artisanal small-scale gold mining (ASM), popularly termed “galamsey” in Ghana, has, for decades, impacted adversely upon water bodies, soils, wildlife, human health, and safety. A lack of understanding, however, of the types of galamsey, their operational attributes, and their relative impacts has unfortunately hindered an effective policy response despite num...

New data mining tools and algorithms are available for vertical data mining communities for scalable and efficient data mining to discover the hidden nuggets from huge repositories of data. Most of the traditional data mining algorithms do not scale on these huge datasets. This is due to insufficient computational resources, currently available on a single machine for running these applications...

Geoscience studies produce data from various observations , experiments, and simulations at an enormous rate. Exploratory data mining extracts \content information" from massive geoscientiic datasets to extract knowledge and provide a compact summary of the dataset. In this paper, we discuss how database query processing and distributed object management techniques can be used to facilitate geo...