In many scientific and engineering fields, there are a number of data sets uncontrollable and hard to handle because the nature of measurement of a performance variable may often be destructive or very expensive, which are known as sets of noise factors. Although these noise factors, which may not be controlled by manufacturing and cost reasons, are merged as a key problem of data mining (DM) a...

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...

Heart disease prediction is designed to support clinicians in their diagnosis. We proposed a method for classifying the heart disease data. The patient’s record is predicted to find if they have symptoms of heart disease through Data mining. It is essential to find the best fit classification algorithm that has greater accuracy on classification in the case of heart disease prediction. Since th...

The paper presents and compares the data mining techniques for selection of the diagnostic features in the problem of blood cell recognition in leukemia. Different techniques are compared, including the linear SVM ranking, correlation and statistical analysis of centers and variances of clusters corresponding to different classes. We have applied radial kernel SVM as the classifier. The results...

Background: In the current study, a hybrid feature selection approach involving filter and wrapper methods is applied to some bioscience databases with various records, attributes and classes; hence, this strategy enjoys the advantages of both methods such as fast execution, generality, and accuracy. The purpose is diagnosing of the disease status and estimating of the patient survival. Method...

The ease of collecting data about customers through the Internet has facilitated the process of developing large repositories of data. These data can and do contain patterns that are useful for the decision maker. Knowledge discovery and data mining methods have been widely used to extract these patterns. It is acknowledged that about 80% of the resources in a majority of data mining applicatio...

Organizations have used Customer Lifetime Value (CLV) as an appropriate pattern to classify their customers. Data mining techniques have enabled organizations to analyze their customers’ behaviors more quantitatively. This research has been carried out to cluster customers based on factors of CLV model including length, recency, frequency, and monetary (LRFM) through data mining. Based on LRFM,...

Due to today’s advancement in technology and businesses, fraud detection has become a critical component of financial transactions. Considering vast amounts of data in large datasets, it becomes more difficult to detect fraud transactions manually. In this research, we propose a combined method using both data mining and statistical tasks, utilizing feature selection, resampling and cost-...