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Full Description
This book discusses the fascinating world of data science and cases in sustainability focusing on the properties of two modern data science fields—data mining and big data analytics—along with their interdependencies, emphasizing sustainable applications that directly address topics related to SDG 9 (Industry, Innovation and Infrastructure). It explains the practical understanding of all the techniques and tools of data mining and big data analytics, along with their mathematical foundations and sustainable applications. Each of these two techniques plays an important part in revealing buried information from massive volume of data. The book emphasizes the interdisciplinary character of data science, relying on topics such as computer science, statistics, physics, economics and more to provide readers with a comprehensive understanding. It carefully takes the readers through the data science pipeline, including data collection, preparation, analysis, management, visualization and storage of high volumes of stationary and non-stationary data and its sustainable applications.
The second of the two volumes, the book digs into the methodologies and algorithms utilized at each level, including key learning mechanisms, association analysis, classification, clustering and outlier analysis. Furthermore, it provides insights on the growing discipline of deep learning and the utilization of distributed systems to handle large amounts of data. Case studies demonstrate the sustainable uses of data in a variety of fields from a big data perspective. Emphasizes the need of data science in handling this "big data" flood, it dives into the technological complexities of massive data storage and processing, emphasizing advances in artificial intelligence that enable successful analysis. Data mining and big data analysis approaches are thoroughly examined in the book, emphasizing their interplay and unique contributions to unravelling the secrets concealed inside massive data sets. The book provides a clear roadmap for navigating the complex world of big data analysis, providing readers with the information and tools they need to gain useful insights from the ever-increasing types of data and, ultimately, to shape the future through data-driven real time decisions.
Contents
1. Knowledge Discovery from Databases.- 2. Attributes, Data Sets and Data Storage.- 3. Data Mining Systems.- 4. Data Mining Tasks.- 5. Associations and Correlations: Frequent Pattern Generation.- 6. Efficient and Scalable Frequent Itemset Generation.- 7. Incremental Rule Mining.- 8. From Association Mining to Correlation Analysis.- 9. Classification.- 10. Prediction.- 11. Cluster Analysis Techniques.- 12. Advanced Clustering Techniques.- 13. Anomaly Detection.- 14. Anomaly Detection Techniques.- 15. Distributed Data Mining.- 16. Advantages and Disadvantages of Data Mining.- 17. What Is Big Data?.- 18. Importance of Big Data.- 19. Big Data Platforms.-20. Machine Learning Strategies and Challenges for Big Data.- 21. Solving Big Problems with Big Data.- 22. Conclusions and Discussions.- 23. Epilogues.
