Abstract
This paper analyses deep learning and traditional data mining and machine learning methods; compares the advantages and disadvantage of the traditional methods; introduces enterprise needs, systems and data, IT challenges, and Big Data in an extended service infrastructure. The feasibility and challenges of the applications of deep learning and traditional data mining and machine learning methods in Big Data analytics are also analyzed and presented.
Highlights
Data mining focuses on the knowledge discovery of data
Deep Learning is a new area in machine learning research, which has been introduced with the objective of moving machine learning closer to one of its original goals — artificial intelligence
Deep learning has the potential in dealing with big data there are challenges
Summary
Data mining focuses on the knowledge discovery of data. Machine learning concentrates on prediction based on training and learning. The most widely used method in dimensionality reduction is principal component analysis (PCA). Redundancy means that some of the variables are correlated with one another Because of this redundancy, PCA can be used to reduce the observed variables into a smaller number of principal components [3]. PCA can be used to reduce the observed variables into a smaller number of principal components [3] Factor analysis is another method for dimensionality reduction. It is useful for understanding the underlying reasons for the correlations among a group of variables. The main applications of factor analysis are reducing the number of variables and detecting structure in the relationships among variables. The purposes of this paper are to 1) analyze deep learning and traditional data mining and machine learning methods (including k-means, k-nearest neighbor, support vector machines, decision trees, logistic regression, Naive Bayes, neural networks, bagging, boosting, and random forests); 2) compares the advantages and disadvantage of the traditional methods; 3) introduces enterprise needs, systems and data, IT challenges, and Big Data in an extended service infrastructure; and 4) discuss the feasibility and challenges of the applications of deep learning and traditional data mining and machine learning methods in Big Data analytics
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