In this paper, we solve the customer credit card churn prediction via data mining. We developed an ensemble system incorporating majority voting and involving Multilayer Perceptron (MLP), Logistic Regression (LR), decision trees (J48), Random Forest (RF), Radial Basis Function (RBF) network and...

In this paper, we solve the customer credit card churn prediction via data mining. We developed an ensemble system incorporating majority voting and involving Multilayer Perceptron (MLP), Logistic Regression (LR), decision trees (J48), Random Forest (RF), Radial Basis Function (RBF) network and...

Logistic regression (LR) continues to be one of the most widely used methods in data mining in general and binary data classification in particular. This paper is focused on providing an overview of the most important aspects of LR when used in data analysis, specifically from an algorithmic and...

Association rule mining is applied to large databases to identify product associations. In the resulting large number of rules, interestingness is difficult to determine. Researchers have defined various measures of 'interestingness' such as support, confidence, lift and gain. Support is the...

The multidimensional data model can be effectively utilised for analysing huge and detailed meteorological datasets forecasted by numerical weather prediction (NWP) model. The model cannot predict any weather event directly. The output products of model are interpreted by man-machine mix to...

Genome data mining and knowledge extraction is an important problem in bioinformatics. Some research work has been done for genome identification based on exact matching of n-grams. However, in most real world biological problems, it may not be feasible to have an exact match, so approximate...

Several computational approaches try to help the researchers to establish the correspondence between pairs of proteins of multiple 2D gel images. In this work, an approach based on block-matching techniques is developed. Its main characteristic is that it does not need to perform the whole...

In previous work, a novel approach to data clustering based on quantum evolutionary algorithm has been proposed. In a comparison to other evolutionary clustering algorithms, the approach showed a high performance in terms of effectiveness and quality of found clusters. Although the approach is...

Data mining has become an important topic in effective analysis of gene expression data due to its wide application in the biomedical industry. A gene cluster is a set of two or more genes that serve to encode for the same or similar products. Gene clustering is the process of grouping related...

Today, the data related to business, finance and healthcare pose problems for Privacy-Preserving Data Mining (PPDM). Privacy regulations and concerns prevent data owners from sharing data for mining purposes. To circumvent this problem, data owners must design strategies to meet privacy...