A data set is sparse if the number of samples in a data set is not sufficient to model the data accurately. Recent research emphasized interest in applying data mining and feature selection techniques to real world problems, many of which are characterized as sparse data sets. The purpose of this research is to define new techniques for feature selection in order to improve classification accur...

New technologies have led to an “explosion” of data available to document states and processes in very many fields. Tools of data mining are being used to extract relevant information. If this information is used in decision making, analytical statistics can provide formal tests comparing the outcomes of different scenarios. Statistics has traditionally dealt with limited information, both in t...

The (randomized) real-valued negative selection algorithm is an anomaly detection approach, inspired by the negative selection immune system principle. The algorithm was proposed to overcome scaling problems inherent in the hamming shape-space negative selection algorithm. In this paper, we investigate termination behavior of the realvalued negative selection algorithm with variable-sized detec...

The contribution of data mining to education as well as research in this area is done on a variety of levels and can affect the instructors’ approach to learning. This particular study focuses on problems associated with classification and attribute selection. An effort to forecast the results takes place before the educational process ends in order to prevent a potential learning failure. The ...

We present the relational database EDULISS (EDinburgh University Ligand Selection System), which stores structural, physicochemical and pharmacophoric properties of small molecules. The database comprises a collection of over 4 million commercially available compounds from 28 different suppliers. A user-friendly web-based interface for EDULISS (available at http://eduliss.bch.ed.ac.uk/) has bee...

Purpose – Churn prediction is a very important task for successful customer relationship management. In general, churn prediction can be achieved by many data mining techniques. However, during data mining, dimensionality reduction (or feature selection) and data reduction are the two important data preprocessing steps. In particular, the aims of feature selection and data reduction are to filt...

This paper describes about the performance analysis of different data mining classifiers before and after feature selection on binomial data set. Three data mining classifiers Logistic Regression, SVM and Neural Network classifiers are considered in this paper for classification. The Congressional Voting Records data set is a binomial data set investigated in this study is taken from UCI machin...

The selection of a study program is unique opportunity for student. STMIK IKMI Cirebon now KIP Kuliah provider, offering three program. research problem the unavailability model student interest in program, so it necessary to carry out an by applying algorithm classification model. used as comparison Decision Tree (C4.5), Naive Bayes, k-Nearest Neighbor and Support Vector Machine. applies Optim...

a r t i c l e i n f o Keywords: Data mining Financial fraud detection Feature selection t-statistic Neural networks SVM GP Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 C...

The heterogeneity and scale of the data generated by high throughput genotyping association studies calls for seamless access to respective distributed data sources. Toward this end the utilization of state of the art data resource management and integration methodologies such as Grid and Web Services is of paramount importance for the realization of efficient and secure knowledge discovery sce...