Artisanal and small-scale gold mining (ASGM) is growing in many regions of the world including Ghana. The problems in these communities are complex and multi-faceted. To help increase understanding of such problems, and to enable consensus-building and effective translation of scientific findings to stakeholders, help inform policies, and ultimately improve decision making, we utilized an Integ...

Spatio-temper-al data mining involves exti-acting and analyzing useful information embedded in a large spatio temporal database. Cluster analysis, one of the data mining techniques, provides the capabilip to investigate the spatio-temporal variation of data. Previous studies in cluster analysis indicate that the optimal number of clusters could be varied with the temporal scale of input data. T...

Parallel decision tree learning is an effective and efficient approach to scaling the decision tree to large data mining application. Aiming at large scale decision tree learning, we present a novel parallel decision tree learning algorithm in MapReduce framework, called PDTSSE (Parallel Decision Tree via Sampling Splitting points with Estimation). We first propose an estimation method for samp...

An important part of large-scale city reconstruction systems is an image clustering algorithm that divides a set of images into groups that should cover only one building each. Those groups then serve as input for structure from motion systems. A variety of approaches for this mining step have been proposed recently, but there is a lack of comparative evaluations and realistic benchmarks. In th...

Mining frequent item sets is a major key process in data mining research. Apriori and many improved algorithms are lowly efficient because they need scan database many times and storage transaction ID in memory, so time and space overhead is very high. Especially, they are lower efficient when they process large scale database. The main task of the improved algorithm is to reduce time and space...

In real world applications sequential algorithms of data mining and data exploration are often unsuitable for datasets with enormous size, high-dimensionality and complex data structure. Grid computing promises unprecedented opportunities for unlimited computing and storage resources. In this context there is the necessity to develop high performance distributed data mining algorithms. However,...

Data indexing is common in data mining when working with high-dimensional, large-scale data sets. Hadoop, a cloud computing project using the MapReduce framework in Java, has become of significant interest in distributed data mining. To resolve problems of globalization, random-write and duration in Hadoop, a data indexing approach on Hadoop using the Java Persistence API (JPA) is elaborated in...

Apriori algorithm mines the data from the large scale data warehouse using association rule mining. In this paper a new algorithm named as Dynamic Apriori (D-Apriori) algorithm is presented. The proposed D-Apriori algorithm incorporates the dynamism in classical Apriori for efficiently mining the frequent itemsets from a large scale database. With the help of experimental results, it is shown t...

The large amount of text data which are continuously produced over time in a variety of large scale applications such as social networks results in massive streams of data. Typically massive text streams are created by very large scale interactions of individuals, or by structured creations of particular kinds of content by dedicated organizations. An example in the latter category would be the...