In this study, a new model is provided for customized privacy in privacy preserving data mining in which the data owners define different levels for privacy for different features. Additionally, in order to improve perturbation methods, a method combined of singular value decomposition (SVD) and feature selection methods is defined so as to benefit from the advantages of both domains. Also, to ...

Studying materials informatics from a data mining perspective can be beneficial for manufacturing and other industrial engineering applications. Predictive data mining technique and machine learning algorithm are combined to design a knowledge discovery system for the selection of engineering materials that meet the design specifications. Predictive method-Naive Bayesian classifier and Machine ...

• Pattern mining based saliency seeds selection method The pattern mining based method is capable of high-level understanding and recognizes discriminative and representative saliency patterns, thus output more accurate saliency seeds. • ERW based seeds propagation method The proposed External Random Walk (ERW) algorithm in one hand ensures the diffusion of seeds information to more distant are...

Medical data mining; Biomedical classification; Variance coverage factor; Principal Component Analysis; Multi Filtration Feature Selection Abstract Selection of optimal features is an important area of research in medical data mining systems. In this paper we introduce an efficient four-stage procedure – feature extraction, feature subset selection, feature ranking and classification, called as...

Real life data sets often contain noisy data which makes the subsequent data mining process difficult. The feature selection preprocessing step can be simplified the datasets by eliminating the features that are redundant for classification process, with pertinent features would reduce the size of dataset and afterwards allow more apparent analysis of extracted rules pattern and rules. This pap...

This technical report presents the work carried out at the University of Lugano on TREC 2014 Federated Web Search track. The main motivation behind our approach is to provide better coverage of opinions that are present in federated resources. On the resource selection and vertical selection steps, we apply opinion mining to select opinionated resources/verticals given a user’s query. We do thi...

Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The method has been highlighted in winning solutions of many data mining competitions, such as the Netflix competition, the KDD Cup 2009 and 2010, the UCSD FICO contest 2010, and...

The patient risk prediction model aims at assessing the risk of a patient in developing a target disease based on his/her health profile. As electronic health records (EHRs) become more prevalent, a large number of features can be constructed in order to characterize patient profiles. This wealth of data provides unprecedented opportunities for data mining researchers to address important biome...

Disease-specific risk prediction aims at assessing the risk of a patient in developing a target disease based on his/her health profile. As electronic health records (EHRs) become more prevalent, a large number of features can be constructed in order to characterize patient profiles. This wealth of data provides unprecedented opportunities for data mining researchers to address important biomed...

Data mining is related to large number of databases. Dealing with such large number of datasets may create some obstacles. Such problems can be avoided by using feature selection Technique. Feature selection Technique is a method which selects an optimal subset from original feature set. The implementation is done by removing repetitive features. The underlying structure has been neglected by t...