Learn what Data Mining is and learn how to implement it in your business!

One of the most remarkable abilities the human brain possesses is recognizing patterns and analyzing data. Researchers try to replicate this capacity in computers, and for that there is Data Mining.

In the area of Computer Science, data scientists began after the Second World War and obtained technological results capable of transforming the world in which we live.

Data Mining (DM) is one of these innovative technologies. Therefore, in this article, we will tell you important information about this data science, such as the meaning, steps and applications. In this article we will see:

• What is Data Mining?
• What is the difference between Big Data, Data Warehouse and Data Mining?
• Data Mining Steps;
• Data Mining Techniques;
• Data Mining Tools.

Good learning!

What is Data Mining?

Data Mining is an algorithm used in a large database to recognize patterns and rules that can help in making a decision.

With the accumulation of data and information generated today, a lot of useful knowledge can end up being lost in the midst of it.

To do so, it is necessary to attempt to look at data analytics and patterns, glancing for hidden treasures. That's why we use Data Mining.

This process is composed of 3 areas of knowledge: Classical Statistics, Artificial Intelligence and Machine Learning:

• Classical Statistics is the origin of the main methods used in Mining, such as analysis of variance and normal distribution.
• Artificial Intelligence seeks to analyze large data sets in real time as the human brain.
• Machine Learning is the combination of these two concepts. Computers are induced to make decisions by recognizing statistical patterns and making predictions with business intelligence. 

Source

Data Mining became popular in the 1990s. At that time, traditional techniques were no longer effective for storing all of an organization's data.

In this context, DM has become one of the most promising tools on the market. In addition to providing millionaire savings to companies when collecting data, it is capable of capturing significant information.

Examples

There are lots of data miners that use these applications around the world, so this concept is more present in your daily life than you can imagine.

Check out some of these applications present in your routine below:

• Customer acquisition: Identification of the profile and behavior of potential buyers of a particular product, contributing to attracting new customers and making assertive business decisions.
• Supermarket: Allocation of products on the shelves according to the consumption profile of its customers, also detecting more valuable offers for consumers.
• Telemarketing: Capturing data from potential customers by analyzing the behavior and profile of leads, designing campaigns so that the sale is assertive.
• Human Resources: Analysis of the competencies of a curriculum. optimizing the candidate selection process.
• Bank: Understanding of market standards, suspicious credit card transactions, purchase patterns and financial data, seeking better customer relationship management.

What is the difference between Big Data, Data Warehouse and Data Mining?

Although they are related concepts, it is not correct to say that Data Mining, Big Data and Data Warehouse have the same meaning:

• Big Data is characterized by the vast amount of random data produced every minute around the world.
• Data Mining is the recognition of patterns within that data. 
• Data Warehouse is the information bank in which all these results are stored.

When we talk about projects, we think about something of great magnitude. This is a mistake, because a project may be involved with small day-to-day activities. We can cite some examples in solving internal problems, negotiating with suppliers, delivering products, implementing systems and strategies. The correct management of the projects brings benefits to the organization, being able to be used in problem-solving. Good project management defines precisely the decision-making processes and also identify causes and effects.

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Data Mining Steps

The Data Mining process takes place through the following steps:

1. Problem definition

Problem definition is the first step in the Data Mining process. In this phase, the objective is to understand the problem and establish the objective to be achieved with the mining process.

2. Data exploration

It is in the exploration of data that the basic statistical tools begin to be used. This is also the stage where experts collect, describe, and explore the data. In addition, the quality of all data is also tested.

3. Data preparation

Data preparation is a process that depends on the source of the data. Thus, depending on the state in which the raw data is, it is necessary to prepare it through methods of filtering, combining and filling empty values.

4. Modeling

This step is directly related to the objective of each Mining process, as it is necessary to choose a predictive modeling technique. Within Data Mining, that guarantees the solution of the proposed problem.

5. Evaluation

The assessment is the most critical phase of the process, as it requires the participation of a group of people specialized in Data Mining. They do business predictive analytics  to assess whether Data Mining has achieved the desired result.

6. Implementation

Implementation is the final stage of the Data Mining project. It is at this stage that the results obtained are imported into databases or other types of directories.

Data Mining Techniques

Data Mining is a very large field, so there is not just one way to find patterns within a large volume of data.

Check below the main techniques used to transform data into information:

1. Association rule discovery

Association rule discovery is one of the most used techniques for knowledge in Data Mining. With it, it is possible to extract a simple solution from complex cases.

This technique consists of analyzing the relationship between items in a certain set of data and finding trends or patterns that can be used to understand the behavior of these data.

A very popular example of association rules is the supermarket. According to this explanation, if a person goes to the supermarket to buy milk and bread, he will also buy butter.

Thus, this technique is very common in marketing campaigns and in shopping center inventory control, as the purchase of product "A" may imply the sale of product "B".

2. Artificial Neural Networks

Artificial neural networks (ANN) present a mathematical model based on the central nervous system. This type of algorithm seeks to solve problems by simulating the behavior and functions of a neuron.

Its operation takes place through dozens or even hundreds of processing units, which are interconnected by communication channels.

In this way, the inputs are similar to dendrites and simulate a stimulus capture area. The data output is compared to neurons and the contact between these elements forms the synapse.

In some ones, the output of one neuron can also become an input signal of another. Therefore, RNAs are capable of generating several types of distinct structures.

3. Decision Trees

Decision trees work like a flowchart, but have the form of a tree. Through this model, it is possible for the user to make decisions from countless possibilities of choice.

These possibilities are tested and work as follows:

The node represents data or problems and each branch has a cluster of solutions based on costs, probabilities and benefits.

Data Mining Tools

Python

Python is a free and open source language. Its learning curve is simple, which makes it easy to use. Regarding Data Mining, users can build datasets and do super complex analysis within minutes.

For simple applications, it is easy to visualize the data, as long as the user has an affinity with basic programming concepts.

Oracle Data Mining

Recommended for more advanced and complex analyses, Oracle is used by large companies, which they use to make accurate predictions on their customer data.

The tool is able to identify sales opportunities in addition to customizing customer profiles the way the user wants.

Learn more! 

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