9/2/2023 0 Comments Data science basics ppt![]() ![]() The reconstruction-based approach is illustrated using autoencoder networks that are part of the deep learning paradigm.Īssociation Analysis: The changes in association analysis are more localized. Existing approaches-statistical, nearest neighbor/density-based, and clustering based-have been retained and updated, while new approaches have been added: reconstruction-based, one-class classification, and information-theoretic. The discussion of evaluation, which occurs in the section on imbalanced classes, has also been updated and improved.Īnomaly Detection: Anomaly detection has been greatly revised and expanded. We have added a separate section on deep networks to address the current developments in this area. The material on Bayesian networks, support vector machines, and artificial neural networks has been significantly expanded. Almost every section of the advanced classification chapter has been significantly updated. The introductory chapter uses the decision tree classifier for illustration, but the discussion on many topics-those that apply across all classification approaches-has been greatly expanded and clarified, including topics such as overfitting, underfitting, the impact of training size, model complexity, model selection, and common pitfalls in model evaluation. The addition of this chapter is a recognition of the importance of this topic and an acknowledgment that a deeper understanding of this area is needed for those analyzing data.Ĭlassification: Some of the most significant improvements in the text have been in the two chapters on classification. This chapter addresses the increasing concern over the validity and reproducibility of results obtained from data analysis. ![]() It supplements the discussions in the other chapters with a discussion of the statistical concepts (statistical significance, p-values, false discovery rate, permutation testing, etc.) relevant to avoiding spurious results, and then illustrates these concepts in the context of data mining techniques. Mathematics background, and no database knowledge is needed.Īssociation analysis, clustering, anomaly detection, and avoiding false discoveries.Īvoiding False Discoveries: A completely new addition in the second edition is a chapter on how to avoid false discoveries and produce valid results, which is novel among other contemporary textbooks on data mining. Solutions for exercises and complete set of lecture slides. ![]() Includes extensive number of integrated examples and.Both theoretical and practical coverage of all data mining ![]()
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