Data mining
Data mining encompasses the techniques and processes used to uncover patterns and insights from vast datasets, serving professionals in fields such as business intelligence, data science, and analytics. This category features a comprehensive collection of texts that cover both the theoretical and practical aspects of data mining, making it an essential resource for learners and practitioners alike seeking to harness the full potential of data.
Introduction to Data Mining, Global Edition
Pang-Ning Tan, Vipin Kumar, Anuj Karpatne, Michael Steinbach
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Handbook of Intelligent and Sustainable Manufacturing: Tools, Principles, and Strategies
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Atlassian Confluence 5 Essentials: Centralize all your organizations documentation in one place using Confluence. From installation to using add-ons, this is a complete, user-friendly tutorial that assumes virtually no prior knowledge.
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Using OpenRefine: With this book on OpenRefine, managing and cleaning your large datasets suddenly got a lot easier! With a cookbook approach and free datasheets included, youll quickly and painlessly improve your data managing capabilities.
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Social Media Mining with R
Nathan Danneman, Richard Heimann
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Developing Churn Models Using Data Mining Techniques and Social Network Analysis
Robert Kopal, Leo Mr¿i¿, Goran Klepac
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Apache Hive Cookbook
Saurabh Chauhan, Shrey Mehrotra, Hanish Bansal
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Mastering Data Mining with Python - Find patterns hidden in your data
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Spark for Data Science
Bikramaditya Singhal, Srinivas Duvvuri
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Mastering Elastic Stack
Yuvraj Gupta, Ravi Kumar Gupta
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Python Data Science Essentials - Second Edition: Learn the fundamentals of Data Science with Python
Alberto Boschetti, Luca Massaron
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Python Deep Learning: Next generation techniques to revolutionize computer vision, AI, speech and data analysis
Daniel Slater, Gianmario Spacagna, Valentino Zocca
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Learning Social Media Analytics with R: Transform data from social media platforms into actionable business insights
Raghav Bali, Dipanjan Sarkar, Tushar Sharma
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Analytics: How to Win with Intelligence
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DATA MINING IN TIME SERIES AND STREAMING DATABASES
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Data mining techniques in financial fraud detection
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Category „Data mining“
Unlocking the secrets contained within vast datasets, data mining is a powerful field that merges statistics, machine learning, and database technology. This category caters to both beginners keen to understand the fundamentals and experienced professionals looking to refine their expertise. Data mining has evolved significantly since its inception, adapting to rapidly advancing technologies and the ever-increasing volumes of data generated daily in our digital world.
The exploration of data mining empowers readers to uncover hidden patterns, relationships, and insights that can drive strategic decision-making across various industries such as finance, healthcare, marketing, and more. By delving into this field, readers can enhance their analytical skills and cultivate a comprehensive understanding of how data influences modern life. These resources provide invaluable techniques, from data preprocessing and visualization to more complex predictive modeling.
As this discipline continues to grow, so do the opportunities for innovation and growth, making it an essential topic for anyone interested in the intersection of technology and analysis. Investing time in data mining literature equips individuals with the knowledge necessary to thrive in an increasingly data-driven world, ensuring they are well-prepared for the challenges and opportunities ahead.
Readers will find works by esteemed authors and reputable publishers who have dedicated their careers to exploring and advancing the techniques of data mining. Their collective wisdom adds depth and credibility to the resources available, inspiring the next generation of data scientists and analysts. Discovering their insights will not only aid in developing a profound understanding of data mining but may also spark new ideas and applications in the field.