Data mining is starting to get more and more interest in the big data community of data analysts, ecommerce owners and everyone who’s already discovered data is the new oil. So today we elucidate data mining techniques, what is data mining and how does it work. 

Let’s dig in! 
(pun intended)

What is data mining? Definition, techniques, and data mining types

What is data mining?

Before jumping ahead to the modern data mining definition, let’s look into how it all began. People have played around with numbers in order to find correlations and patterns for hundreds of years. The earliest documented data mining techniques occurred in the early 1700s with Bayes’ theorem (a formula to calculate events probability) and later in the 1800s with regression analysis (a process of dependent and independent variables relationships estimation).

Before calling it data mining, data scientists used to call it data “fishing” or “dredging”, and unfavoured it as a bad practice. It reminded them of angling for a haul without having any prior hypothesis established. The times have drastically changed, as now data mining does no longer resemble a pursuit of a fleeing prey, but a thorough assessment of data by an all-seeing algorithm.

Here’s what is data mining, also known as knowledge discovery, today:

Data mining is the process of analyzing large data sets in order to find patterns, performed by machine learning, statistics, and database systems techniques.

Information subtracted as a result of a data mining process allows ecommerce companies to find correlations between their customers’ profiles, behaviour, items they’ve selected, elements that triggered purchases and so on. These invaluable data findings allow progressive brands to build better ads and user interfaces, therefore staying ahead of their competitors and earning drastically more money. 

Data mining techniques

Data mining articles provide a number of different data mining classifications according to their techniques and properties. As opposed to the data analytics, which has 4 main types (here’s a great article by the way – 4 Types of Data Analytics to Improve Decision-Making), data mining splits into 2 core categories:

Descriptive modelling & Predictive modelling. 

Descriptive data mining techniques, as you’ve already guessed, involve ways of identifying feasible sequences and representing them in various ways. Descriptive data modelling includes:

  • Clustering 
  • Summarization
  • Sequence discovery
  • Association rules

Predictive data mining, on the other hand, makes a prognosis of future events based on historical data containing in data sets, which is just another name for any collection or assortment of data. Predictive data modelling includes:  

  • Regression
  • Prediction
  • Classification
  • Time series analysis

Now that you met 2 data mining types and their techniques, let’s look into how beneficial is data mining for business analytics.

Data mining for business analytics

The peculiar properties of the data mining process allow businesses to find information that couldn’t be dug otherwise. Why? Well judge for yourself, data mining uses all available data, including historical which allows for a much broader range of discoveries, as opposed to using data gathered for individual research. Imagine the opportunity to analyze the behaviour patterns of your highest-paying customers over the last couple of years.  

Sounds marvellous, doesn’t it? Well, that’s not the end of the list of data mining feature’s perks. On top of the ability to analyze large sets of data, data mining employs traditional and non-traditional techniques of pattern recognition, with latter presenting powerful insights other methods wouldn’t.

Data mining is no longer just a good idea to consider, companies are adapting it, and adapting it fast. It is definitely an investment, but the fruits it bears are worth it. The prominent data mining examples include Delta, a major US airline, which uses data mining to analyze all reviews and comments from their customers on Twitter, sending negative feedback straight to the support. 

If, say, you are unhappy about the lost luggage, a dedicated airline representative will greet you with an upgraded first-class ticket and a promise to return your belongings as soon as you touch the land. 

All-known Netflix used data mining in order to determine what makes a show profitable and predicted the House of Cards would be a huge success before their competitors did, thus booking 2 seasons ahead and earning a looot of money. 

And have you ever wondered what lets Starbucks open coffeeshops a block away from each other and still manage to keep them packed? Data mining.

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Regardless of whether we like it or not, data mining works, and it works wonders. Not on its own, no. But the insights presented by this powerful tool in the hands of strategic thinkers moove mountains and bring an enormous return on investment. 

To get more meaningful insights on the world of data, read these: