Introduction to Data Mining

Data mining, also known as KDD (knowledge discovery in database), is the process of uncovering patterns and other valuable information from large data sets.

The example using TensorFlow shows extracting relevant insights by combining data mining with data analytics and visualization tools. Advances within artificial intelligence only continue to expedite adoption across industries.

Why Data Mining
Given the evolution of data warehousing technology and the growth of big data, adoption of data mining techniques has rapidly accelerated over the last couple of decades, assisting companies by transforming their raw data into useful knowledge. However, despite the fact that technology continuously evolves to handle data at a large-scale, leaders still face challenges with scalability and automation.

What Data Mining Does
Data mining has improved organizational decision-making through insightful data analyses. The data mining techniques that underpin these analyses can be divided into two main purposes; These methods are used to organize and filter data, surfacing the most interesting information, from fraud detection to user behaviors, bottlenecks, and even security breaches.




      Waiter: “How do you like your steak, sir?”    
      Sir: “Like winning an argument with my wife.”    
      Waiter: “Rare it is!”