Data mining adalah suatu proses ekstraksi atau penggalian data dan informasi yang besar, yang belum diketahui sebelumnya, namun dapat dipahami dan berguna dari database yang besar serta digunakan untuk membuat suatu keputusanbisnis yang sangat penting. Data mining menggambarkan sebuah pengumpulan teknik-teknik dengan tujuan untuk menemukan pola-pola yang tidak diketahui pada data yang telah dikumpulkan. Data mining memungkinkan pemakai menemukan pengetahuan dalam data database yang tidak mungkin diketahui keberadaanya oleh pemakai.
Berikut beberapa koleksi Ebook tentang Data Mining yang bagus untuk dibaca dan dipelajari mahasiswa :
1. Data Mining and Statistics for Decision Making by Stéphane Tufféry
Description :
Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization’s need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives.
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2. Data Mining For Dummies by Meta S. Brown
Description :
Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day–to–day operations allows savvy decision–makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business′s entire paradigm for a more successful outcome.
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3. Data Mining: Practical Machine Learning Tools and Techniques,3rd Edition by Ian H. Witten
Description :
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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4. Data Mining With Decision Trees : Theory and Applications by Lior Rokach
Description :
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.
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5. Encyclopedia of Data Warehousing and Mining (2 Volumes) by John Wang
Description :
Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead.
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6. Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More by Matthew A. Russell
Description :
How can you tap into the wealth of social web data to discover who’s making connections with whom, what they’re talking about, and where they’re located? With this expanded and thoroughly revised edition, you’ll learn how to acquire, analyze, and summarize data from all corners of the social web, including Facebook, Twitter, LinkedIn, Google+, GitHub, email, websites, and blogs.
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7. R and Data Mining: Examples and Case Studies by Yanchang Zhao
Description :
This book guides R users into data mining and helps data miners who use R in their work. It provides a how-to method using R for data mining applications from academia to industry. The book helps researchers in the field of data mining, postgraduate students who are interested in data mining, and data miners and analysts from industry.
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