Feature Selection for Knowledge Discovery and Data Mining

1998 ◽  
Author(s):  
Huan Liu ◽  
Hiroshi Motoda
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Knowledge Discovery ◽  
Selection For

A Perturbation Method Based on Singular Value Decomposition and Feature Selection for Privacy Preserving Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Mohammad Reza Keyvanpour ◽  
Somayyeh Seifi Moradi
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Singular Value Decomposition ◽  
Perturbation Method ◽  
Privacy Preserving ◽  
Singular Value ◽  
Privacy Preserving Data Mining ◽  
Selection For ◽  
Value Decomposition ◽  
Different Levels

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 assess the amount of distortion created by the proposed perturbation method, new distortion criteria are defined in which the amount of created distortion in the process of feature selection is considered based on the value of privacy in each feature. Different tests and results analysis show that offered method based on this model compared to previous approaches, caused the improved privacy, accuracy of mining results and efficiency of privacy preserving data mining systems.

scholarly journals Latest Tools for Data Mining and Machine Learning

2019 ◽  
Vol 8 (9S) ◽  
pp. 18-23 ◽  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Feature Selection ◽  
Open Source ◽  
Predictive Analysis ◽  
Learning Tools ◽  
Pros And Cons ◽  
Selection For ◽  
Extract Information

Nowadays, Data Mining is used everywhere for extracting information from the data and in turn, acquires knowledge for decision making. Data Mining analyzes patterns which are used to extract information and knowledge for making decisions. Many open source and licensed tools like Weka, RapidMiner, KNIME, and Orange are available for Data Mining and predictive analysis. This paper discusses about different tools available for Data Mining and Machine Learning, followed by the description, pros and cons of these tools. The article provides details of all the algorithms like classification, regression, characterization, discretization, clustering, visualization and feature selection for Data Mining and Machine Learning tools. It will help people for efficient decision making and suggests which tool is suitable according to their requirement.

Simultaneous Feature Selection and Tuple Selection for Efficient Classification

2010 ◽  
pp. 270-285
Author(s):  
Manoranjan Dash ◽  
Vivekanand Gopalkrishnan
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Distance Measure ◽  
Microarray Gene Expression ◽  
Research Areas ◽  
Microarray Gene ◽  
Selection For ◽  
Learning Data

Feature selection and tuple selection help the classifier to focus to achieve similar (or even better) accuracy as compared to the classification without feature selection and tuple selection. Although feature selection and tuple selection have been studied earlier in various research areas such as machine learning, data mining, and so on, they have rarely been studied together. The contribution of this chapter is that the authors propose a novel distance measure to select the most representative features and tuples. Their experiments are conducted over some microarray gene expression datasets, UCI machine learning and KDD datasets. Results show that the proposed method outperforms the existing methods quite significantly.

Data Mining and Knowledge Discovery in Metabolomics Armin

2008 ◽  
pp. 1759-1783
Author(s):  
Christian Baumgartner ◽  
Armin Graber
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Knowledge Discovery ◽  
Biomedical Application ◽  
Life Sciences ◽  
Background Information ◽  
Data Generation ◽  
Discovery Process ◽  
Mining Community ◽  
Preclinical And Clinical Studies

This chapter provides an overview of the knowledge discovery process in metabolomics, a young discipline in the life sciences arena. It introduces two emerging bioanalytical concepts for generating biomolecular information, followed by various data mining and information retrieval procedures such as feature selection, classification, clustering and biochemical interpretation of mined data, illustrated by real examples from preclinical and clinical studies. The authors trust that this chapter will provide an acceptable balance between bioanalytics background information, essential to understanding the complexity of data generation, and information on data mining principals, specific methods and processes, and biomedical application. Thus, this chapter is anticipated to appeal to those with a metabolomics background as well as to basic researchers within the data mining community who are interested in novel life science applications.

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