In today’s era of big data, there are more and more data sources, including websites, enterprise applications, social media, mobile devices and the Internet of things, as well as more and more data generated by the Internet of things. For enterprises, how to obtain real business value from these data becomes more and more important,data mining It is a targeted link in the process of data analysis. Excellent data analysts will use intelligent mining operations to make complex data more convenient.
In terminology, data mining is usually used to collect, extract, store and analyze data and other large-scale data processing activities. It can also be used to help improve the decision-making of applications and technologies, such as artificial intelligence, machine learning and business intelligence.
Today, let’s talk about what data mining technology can bring to enterprises.
Find valuable data
1、 Definition of data mining
Data mining refers to determining trends and patterns through a large number of programs and data analysis, and establishing relationships to solve business problems. In other words, data mining is extracted from a large number of incomplete, noisy, fuzzy and random data. What people don’t know in advance is a potentially useful data and knowledge process.
2、 Difference from data analysis
Both data analysis and data mining discover knowledge from database, so we call them data analysis and data mining. But strictly speaking, data mining is the real knowledge discovery in database (KDD).
Data analysis is based on the database, and obtains the knowledge of data representation from the database through statistics, calculation, sampling and other relevant methods, that is, obtains some representative information from the database. Data mining is a technology that obtains deep knowledge (such as rule or attribute prediction) from database through machine learning or mathematical algorithm.
3、 Data mining has both advantages and disadvantages
In principle, data mining can be applied to any type of data repository and transient data (such as data flow), such as database, data warehouse, data market, transaction database, spatial database (such as map), engineering design data (such as architectural design), multimedia data (text, image, video, audio), network and time series database.
Therefore, data mining has the following characteristics:
1. The data set is large and incomplete.
The data set required for data mining is very large. The larger the data set, the closer the obtained law is to the correct actual law, and the more accurate the result is. In addition, data are often incomplete.
2. The data is inaccurate.
The inaccuracy of data mining is mainly caused by noise data. For example, in business, users may provide false data; In the factory environment, normal data are often subject to electromagnetic or radiation interference exceeding the normal value. These abnormal and absolutely impossible data are called noise, which will lead to inaccurate data mining.
3. The data is fuzzy and random.
Data mining fuzzy random. The ambiguity here may be related to inaccuracy. Because the data is inaccurate, we can only observe the data as a whole, or because of privacy information, we can’t know some specific content. At this time, if we want to do relevant analysis operations, we can only do some general analysis and can’t make accurate judgment.
There are two explanations for the randomness of the data. First, the randomness of data collection; We don’t know what the user filled in. Second, the analysis results are random. If the data is handed over to the machine for judgment and learning, all operations belong to gray box operation.
Thus, as a powerful tool, data mining has its advantages and disadvantages. Only when used at the right time can we get twice the result with half the effort.
4、 The sustainable development of business data mining technology can not be ignored
1. Development model is more convenient
For many years, first principle models are the most classic models in the field of science and engineering. For example, if you want to know the distance of a car from starting to stable speed, you must first calculate the time from starting to stable speed, stable speed, acceleration and other parameters; Then the model is established by using Newton’s second law (or other physical formula); Finally, according to the vehicle test results, the equations are listed to calculate the model parameters.
Through this process, you are equivalent to learning a knowledge – the specific model of the car from starting to stable speed. Then the starting parameters of the vehicle are input into the model to automatically calculate the driving distance before the vehicle reaches the stable speed.
However, in the idea of data mining, knowledge learning does not need the professional knowledge of modeling specific problems. If I record the distance from starting to steady speed of 100 models and vehicles with similar performance, I can calculate the average value of these 100 data and get the results. Obviously, this process is directly data oriented, or we develop models directly from data.
This is actually a simulation of people’s initial learning process. For example, if you want to predict how long it will take a person to run 100 meters, you must estimate how long it will take a person like him to run 100 meters instead of using Newton’s law.
2. Maturity of computer technology
Data mining theory covers a wide range, in fact, it comes from many disciplines. For example, the modeling part mainly comes from statistics and machine learning. Statistical methods are model driven, and usually establish models that can generate data; Machine learning is algorithm driven, which allows computers to discover knowledge by executing algorithms.
With the development of Internet tools, the cost of sharing and cooperation has been greatly reduced. We use mobile phones to chat, shop, brush short videos, watch news and other daily unintentional behaviors every day, providing a large amount of data for the Internet industry. This data is usually collected and stored in large data repositories. Without powerful tools, we can’t understand them. The emergence of data mining technology solves this problem. It can extract valuable information from massive data as an important basis for decision-making.
3. Forecast the production and sales of enterprises
The real value of data mining is that it can mine hidden gemstones in the form of patterns and relationships in data, which can be used to predict the significant impact on enterprises. For example, if a company determines a specific marketing activity, resulting in a high sales volume of a specific model of products in some regions of the country, but not in other regions, it can readjust the advertising activities in the future to achieve the maximum return.
The advantages of this technology may vary depending on the type of business and objectives. For example, retail sales and marketing managers may mine customer information in different ways to improve conversion rates, which is very different from airlines or financial services.
No matter which industry, the data mining applied to sales patterns and customer behavior in the past can be used to create models to predict future sales and behavior. Data mining also helps eliminate activities that may harm enterprises. For example, you can use data mining to improve product security or detect fraud in insurance and financial services transactions.
4、 Data mining tools
Data mining system can be independent of data warehouse system. However, in order to improve the mining efficiency, generally based on the data warehouse, mining algorithms are used to mine potential patterns from the prepared data, so as to help decision makers adjust market strategies, reduce risks and make correct decisions.
Predicting the future does not rely on any magic or heavenly book, but uses scientific methods and advanced smartbi data mining scientific platform to analyze and mine the secrets hidden in a large amount of data, reveal the relationship between data and judge the development trend of affairs.
Traditional data analysis reveals known. Past data relationships, while data mining reveals the unknown. Future data relationships; Traditional data analysis adopts computer technology, while data mining not only adopts computer technology, but also involves statistics, model algorithm and other technologies. Because data mining finds future information, it is mainly used for prediction! Predict the future sales volume of the company, predict the future price of products, etc.
Smartbi data mining science platform provides one-stop data mining services, covering the whole life cycle of data preprocessing, machine learning algorithm application, model training, evaluation, deployment and service release.
It is widely used in various fields, including enterprise operation, production control, market analysis, engineering design, urban planning and scientific exploration, mining useful information and knowledge from a large amount of data to better guide our work; This function has the following features:
1. Spark distributed cloud computing.
2. Intuitive flow modeling and drag operation.
3. Practical statistical analysis. Explore visual data.
4. Mature machine learning algorithms such as prediction and clustering.
5. The algorithm is simple to configure and the threshold is low.
6. Support Python extension.
7. The model is centrally managed and easy to publish to the Bi platform.
Smartbi prediction analysis of smart smart software collects more than 50 kinds of data mining algorithm components, mainly including rich algorithm components such as classification, clustering, relevant rules and regression; Supports Java and python algorithm extensions and can customize user scenarios.
In the process of enterprise development, the data generated is increasing. Good data mining tools can effectively process and analyze the enterprise’s data, so as to “woodpecker” check and fill in the loopholes of the enterprise, make the enterprise develop more healthily, and predict and analyze through data mining to better grasp the future trend.