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Data Mining History

Data MiningLearn basics of Data Mining

Data Mining is the process of extracting predictive information from a large database. It is used by many big companies to make future decisions. LiveEdu is a great place to start learning or improve your data mining skills with our section dedicated to data mining tutorials and resources. Here you can watch live stream of how data mining works. You can also search for data mining topic in our video library. Join the data mining community!


Data Mining Introduction

Data Mining is the process of extracting predictive information from large database. It is used by many big companies to make future decisions and equip themselves with knowledge that is not easy to find and understand on normal circumstances. Usage of Data Mining also allows companies to be proactive and knowledge-driven rather than using their instincts. Companies use Data Mining tool to extract hidden patterns ,find predictive information and use them to their advantage.

Data mining process is very important when it comes to finding hidden information in massive quantities and it is extremely relevant in the current industry. Almost every big company use data mining to retrieve meaningful information and use it to make important decisions.

History of Data Mining

Data Mining history an interesting one. Let’s go through some key events in Data Mining that you should know.

The term "Data Mining" was introduced in the 1990s, but data mining is the evolution of a field with a long history.

Early methods of identifying patterns in data include Bayes' theorem (the 1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology have increased data collection, storage, and manipulations. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, clustering, genetic algorithms (the 1950s), decision trees (1960s), and support vector machines (1990s).

Data Mining Tools

With Data mining tools, you can do a lot of stuff. These tools will help you improve your working process and enable you to focus on things that matter. Let’s list the 5 best Data Mining tools.

  • R: R is a free data mining tool that can help you to work efficiently with tons of data. It is an open source tool and it is free. Anyone can use it as it doesn’t require any programming language. Importantly, there are plenty of pre-built packages that you can use to execute your requirements.
  • IBM SPSS Modeler: IBM SPSS Modeler is made for working on large-scale projects. It offers good workbench and visual interface for easy workflow. Also, you don’t need a programming knowledge to work with this tool. If you know programming, it will obviously make things easier for you.
  • Python Python is a great tool to work with data. It is also an open source and it is free to use. To use Python, you need to have programming language knowledge.
  • Spark Spark is one of the easy way to handle data mining task. It was built using Python and it is used on large projects.
  • Orange: Orange is a simple software suite of machine learning that allows easy data manipulation. It is ideal for beginners and it is also free to use.

Education Ecosystem Data Mining Project Creators

If you are wondering where to get started to learn Data Mining, then the best way is to watch Data Mining Project Creators on Education Ecosystem. Let’s list the top 5 data mining Project Creators on Education Ecosystem.

Project Creators!

Data Mining Best Books

There are plenty of Data Mingin books available online. The best way to start learning data mining is to invest in the books. So, why the wait? Let’s go through the best books for learning Data Mining . The books are categorized into Beginner, Intermediate and Advanced. So pick the book that best suits you.

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    Principles of Data Mining (Adaptive Computation and Machine Learning)

    by David J. Hand, Heikki Mannila, Padhraic Smyth

    This book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

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    Data Mining and Analysis: Fundamental Concepts and Algorithms

    by Mohammed J. Zaki, Wagner Meira Jr

    The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics.

  • Book cover

    Introduction to Data Mining

    by Michael Steinbach, Vipin Kumar, Pang-Ning Tan

    Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

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    Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

    by Foster Provost, Tom Fawcett

    This guide also helps you understand the many data-mining techniques in use today. From this book, you’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

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    Data Mining: The Textbook

    by Charu C. Aggarwal

    This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks.

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    Data Mining: Concepts and Techniques, Second Edition (The Morgan Kaufmann Series in Data Management Systems)

    by Micheline Kamber, Jiawei Han, Jian Pei

    This book will take you through a comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data, updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning and dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects

Data Mining Projects

The best way to learn is to evolve yourself with Projects. Let’s take a look at some of the best Data Mining projects that you can follow. You can also find Data Mining projects on Education Ecosystem. If you are interested, check the Education Ecosystem Data Mining Project Creators section for more information.

Sample programs in Data mining will help you get started with data mining. The GitHub repository contains everything that you need to get started.

Explore this project!

Orange 3 Data Mining suite is a component-based data mining software. With the repository, you can explore the code and learn a lot about the different aspects of exploration, preprocessing, data visualization and modeling techniques.

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Hearthbreaker is one of the finest examples of Data Mining solution. It is a simple open source simulator with the focus of using both data mining and machine learning in the Blizzard’s Hearthstone: Heroes of WarCraft.

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A CPU and GPU based accelerated matrix library used for data mining.

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A snake charmer is a portable Python workbench that can be used with Salt, VirtualBox, and Vagrant.

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Data Mining Community

Data Mining community is one of the biggest when it comes to growth and numbers. If you want to learn Data Mining, it is best to be the part of the community and contribute accordingly. Let’s list some of the Data Mining community you can become part of.

  • Education Ecosystem Here you can find all the awesome Project Creators who love to share their knowledge about Data Warehouse.

Data Mining Gurus

  • Gregory Piatetsky

    Gregory Piatetsky

    Gregory Piatetsky is the KDuggets Editor and also a Data Mining and Analytics expert. He is also the co-founder of SIGKDD and KDD.

  • Russell Steele

    Russell Steele is an expert in Data mining, Data analysis and Biostatistics. He works as an Associate Professor in McGill University.

    Russell Steele
  • Maiga Chang

    Maiga Chang

    Maiga Chang is from Athabasca University and is a Data Mining expert. He works as an associate professor with a focus on Affective computing, Behavior analysis, and game-based learning.

  • Cyrus Shahabi

    Cyrus Shahabi is a web data mining expert. He is also interested in WWW and internet databases and is working as Assistant professor for computer science in University of Southern Califronia.

    Cyrus Shahabi
  • Dunwei Grant

    Dunwei Grant

    Dunwei Grant Wei is a researcher in natural language processing and helps in the building applications for data mining. He works as an associate professor in Athabasca University.

Data Mining Conferences

As Data Mining is a trending topic in the market, there are many conferences out there that you can attend. Let’s list some of the best data visualization conferences out there.