The History of Data Mining

The History of Data Mining

The History of Data Mining

Data mining is everywhere, but its story starts many years before Moneyball and Edward Snowden. The following are major milestones and “firsts” in the history of data mining plus how it’s evolved and blended with data science and big data.

Data mining is the computational process of exploring and uncovering patterns in large data sets a.k.a. Big Data. It’s a subfield of computer science which blends many techniques from statistics, data science, database theory and machine learning.

1763Thomas Bayes’ paper is published posthumously regarding a theorem for relating current probability to prior probability called the Bayes’ theorem. It is fundamental to data mining and probability, since it allows understanding of complex realities based on estimated probabilities.

1805 Adrien-Marie Legendre and Carl Friedrich Gauss apply regression to determine the orbits of bodies about the Sun (comets and planets). The goal of regression analysis is to estimate the relationships among variables, and the specific method they used in this case is the method of least squares. Regression is one of key tools in data mining.

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1936 This is the dawn of computer age which makes possible the collection and processing of large amounts of data. In a 1936 paper, On Computable Numbers, Alan Turing introduced the idea of a Universal Machine capable of performing computations like our modern day computers. The modern day computer is built on the concepts pioneered by Turing.

1943 Warren McCulloch and Walter Pitts were the first to create a conceptual model of a neural network. In a paper entitled A logical calculus of the ideas immanent in nervous activity, they describe the idea of a neuron in a network. Each of these neurons can do 3 things: receive inputs, process inputs and generate output.

1965 Lawrence J. Fogel formed a new company called Decision Science, Inc. for applications of evolutionary programming. It was the first company specifically applying evolutionary computation to solve real-world problems.

1970s With sophisticated database management systems, it’s possible to store and query terabytes and petabytes of data. In addition, data warehouses allow users to move from a transaction-oriented way of thinking to a more analytical way of viewing the data. However, extracting sophisticated insights from these data warehouses of multidimensional models is very limited.

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1975 John Henry Holland wrote Adaptation in Natural and Artificial Systems, the ground-breaking book on genetic algorithms. It is the book that initiated this field of study, presenting the theoretical foundations and exploring applications.

 



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