In recent years, data mining has managed to attract a lot of attention in the information industry . The main reason is that there is a large amount of data, which can be used, and there is an urgent need to turn this data into useful information and knowledge. Being able to be used later in various applications, such as business management, production control, market analysis, engineering design and scientific exploration.
Data mining is an important topic in the field of artificial intelligence and database research. It refers to the process of revealing hidden , previously unknown and potentially valuable information from a large amount of data. It is also a decision support process, which is based mainly on artificial intelligence, automated analysis of business data, inductive reasoning and the search for potential patterns based on them.
What is data mining?
Data Mining or Data Mining, as it is known in our language, is a technology composed of a method or a set of analytical methods and statistical tools that extract, collect and analyze a large amount of information (data), from a database structured data of a company. In this way, it automatically discovers useful trends, patterns and rules of customer behavior. Data that supports the implementation of marketing plans. Simply put, it extracts useful information from the collected data.
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Data mining is a technology that strongly supports CRM, that is, methods and strategies to form good long-term relationships between companies and clients based on deep knowledge of each client . By analyzing data such as the buying behavior of customers, it is used to classify products, predict purchase rates for a certain segment, and discover the largest amount of data related to products and customers. Data Mining has become indispensable for marketing.
Increased machine power, network expansion, increased open data, and reduced information collection and retention costs have resulted in businesses and individuals obtaining a large amount of information from a variety of types and quality that can be used for data mining. Consequently, data mining is also attracting a lot of attention as an excellent means of effectively using Big Data.
What tools and techniques are used?
Having a lot of information is a great advantage for companies as long as they know how to make the most of it. However, there is no use in having a great treasure if it cannot be reached. The same happens with all the information that reaches the company. In fact, e s necessary to have the appropriate tools and techniques for getting the most out of the collected information . At present, a large number of software has been developed for this purpose.
There are different types of data mining tools available on the market. Most of these software are available in Windows and Unix versions and each has its own strengths and weaknesses. In fact, many of these monitor data and highlight trends from the desktop. Even capturing information that resides outside of databases sometimes. Let’s look at some of the most popular tools below:
- Rapid miner
- XL Miner
As for the techniques used, it is somewhat similar to the tools. There is a variety of them and they are all good. So ensuring that one is better than another could be a bit risky, since that will depend on the purpose that is pursued, which may vary from one company to another. Let’s see below what are the main techniques when talking about data mining:
- Classification analysis
- Association Rules Learning
- Detection of anomalies or outliers
- Cluster analysis
- Regression analysis
What advantages does it bring to companies?
Among the most important advantages that the company obtains from the implementation of data mining we can mention:
- Discover information you didn’t expect to get. Thanks to its operation, it allows making many different combinations of the different data obtained, achieving new discoveries with its results.
- It is capable of quickly and reliably analyzing multiple databaseswith huge amounts of data.
- The results obtained are easy to understandand do not require great technical knowledge for their interpretation.
- Thanks to the information collected and analyzed, it allows the company to classify existing customers inaddition to promoting finding, attracting and retaining new customers.
- It allows companies to try to satisfy the needs of users by offering the products or services they demand. This is because by knowing the trends and search patterns of its customers, the company is in a better position to create the necessary offers to satisfy the needs of its users.
- The obtained models can be verified by statistical analysis. Thanks to this, it can be verified that the results and predictions obtained are reliable.
- Contributes to reducing costs and exploring new businesses. With knowledge, the company avoids the policy of trial and error, which translates into a significant reduction in costs. Besides that it also allows you to venture into new fields according to the patterns observed in users.
What are the stages of Data Mining?
Data mining has become an independent discipline over the past decades. However, to get its best performance it requires a systematic process . This process is essential within data mining to achieve an efficient and goal-oriented way of working. To carry out the knowledge discovery process in a reliable and reproducible way, the CRISP-DM standard has been established as a guideline. The CRISP model comprises 6 necessary phases in data mining.
Business understanding, in this first phase goals are defined and information about tasks is exchanged. In addition, the appropriate procedures for the task are determined. The second phase is the Understanding of data, in this phase the quality and reliability of the data is checked. What data is available? What characteristics were surveyed? Etc. Data Preparation is the third , here the variables are coded or transformed as necessary. And appropriate procedures can be used for missing data. Experience has shown that this phase takes much of the time.
Modeling is the next phase and it is where the necessary procedures are carried out to answer the questions. Generally, different parameters must be varied and different models created. The Evaluation or assessment is the phase of comparison of the models created from the predictive analytics CRISP-DM. For this, several parameters of the quality of the model are used. And finally, the Provision of results or display, the step in which the results obtained are finally summarized, processed and presented in an understandable way.