The Basic Data Mining Techniques and Their Common Applications

Data mining basically involves the sifting of large volumes of data to generate new information. Read more about the techniques and applications in this field.
Data mining, which is a type of artificial intelligence, has been used primarily to analyze scientific and business data for patterns or trends. Data basically comprises raw statistics or facts that are collected through measurement or research, in order to be analyzed. Examples include sale totals, names, places, and phone numbers. For instance, when you go to purchase something from a shop and you are asked to provide your zip code or telephone number at the checkout, essentially that is an exercise in collecting data, which will be used to analyze buying patterns, like how many other people from your area purchased the same product. This branch of computer science helps to determine patterns in data, and allows businesses to predict in what manner consumers will behave in the future.

Helps To Improve Decision-making

Thus, data mining uses predictive techniques to reveal patterns in the data. These patterns have a vital role in the process of decision-making since they expose the areas where improvements can be made in the process. Organizations can use data mining in such a way as to improve profitability and effectiveness of their engagement with customers, improve the management of risk, and detect fraud. In other words, this information assists organizations in making timelier and better decisions.


Here is a brief account of two of the most popular techniques:

Regression: This is the most widely known and the oldest statistical technique that is utilized by the data mining community. Essentially, regression makes use of a dataset to develop a mathematical formula which fits the data. So whenever you want to use the results for predicting future behavioral patterns, all you need to do is just take the new data, and apply it to the formula that has been developed, and you will get your prediction. The greatest limiting factor of this technique is that it works well with only quantitative data that is continuous, such as age, speed, or weight. But if you need to work with data that is categorical, where there is no significant order, such as gender, name, or color, it is better to use a different technique.

Classification: If you need to work with categorical data, or a combination of categorical and continuous numeric, classification analysis will meet your requirements. This technique has the capability to process a more extensive variety of data compared to regression and is therefore increasing in popularity. In addition, the output it provides can be interpreted more easily. Rather than the complex mathematical formula that the regression technique provides, in this you will be provided with a decision tree which requires a sequence of binary decisions.

Applications and Tools

Data mining software is usually divided into two groups by most analysts: applications and tools. While applications implant techniques that are customized to deal with a particular business problem, tools, on the other hand, provide several techniques that can be utilized for any problem.

Irrespective of whether we are cognizant of it or not, our everyday lives are touched by such applications. For instance, practically every monetary transaction we make is processed via an application, in order to detect fraudulence. However, both applications and tools are valuable. Organizations are increasingly using both in an integrated manner to carry out predictive analysis.

How Do They Work Together?

These tools are utilized to ensure the highest level of accuracy possible as well as flexibility. Basically, the effectiveness of these applications is increased via the tools. As no two sets of data or organizations can ever be completely alike, there cannot be a single technique that can provide the best results for everybody. Apart from these software tools providing in-depth techniques, they also offer the flexibility to use any combination of them in order to improve the accuracy of the predictions. Due to the flexibility of these tools, a methodology and a set of guidelines have been devised in order to guide analysts. The CRISP-DM, or the Cross-Industry Standard Process for Data Mining, ensures that your business' results are reliable and accurate. This methodology was devised in conjunction with vendors and practitioners, in order to provide them with guidelines, checklists, objectives, and tasks for each stage of the process.