This paper reviews the application and development trend of data mining

Finebi data mining has a built-in decision tree model that supports the use of various types of values** discretized values (text or discretized values, time). The decision tree obtained from model training is the basis for the results.

Bottom line: The era of big data has come, and the revolution of the Internet is about to begin...

According to the development history of data mining tools, the future trend of data mining system is briefly described.

Hello friends, the future trend of data mining tools or tools is as follows:1Deep Learning in the Era of Big Data:

Extracting useful information from big data requires data mining tools. In the future, deep learning will become the main technology for data mining systems. Through machine learning, systems will be able to automatically process and analyze massive amounts of data to provide businesses and scientists with more accurate conclusions.

2.Analytics and real-time: Data mining systems will increasingly be used for analytics and real-time.

These systems will monitor and analyze data for future trends and situations. Today, these systems are widely used in financial market analysis, and are increasingly used in sales, business intelligence, market intelligence, and healthcare. 3.

Natural Language Processing and Sentiment Analysis: With the rapid development of AI technology, data mining systems will be increasingly used for natural language processing and sentiment analysis. These systems will be able to parse human language and quickly find relevant data to better understand customer sentiment and needs.

4.Enhancement of human intelligence: Data mining systems will increasingly implicitly integrate human intelligence.

By harnessing human intelligence, systems will be able to increase their accuracy and value. This includes using human inspiration to develop new algorithms and methods, as well as using human experience to analyze and interpret data quickly.

Business intelligence is committed to helping enterprises find and solve existing problems, simulate the future development of enterprises established after 1989, assist enterprises to adjust their strategies in time to make better decisions, and enhance the sustainable competitiveness of enterprises.

Analytic Hierarchy Process, Regression, Clustering, Neural Networks, Greedy Algorithms, Principal Component Analysis, Factor Analysis, Grayscale**, etc.

The purpose of data analysis is different from that of data mining, data analysis has a clear analysis group, that is, the group is split, divided and combined in various dimensions to find the problem, while the target group of data mining is uncertain, and we need to analyze more from the internal connection of data, so as to combine business, users, and data for more insight interpretation.

Data analysis is different from data mining, generally speaking, data analysis is based on objective data for continuous verification and assumptions, while data mining has no assumptions, but you also have to give your criteria for judging according to the output of the model.

Analytical framework (hypothesis) + objective problem (data analysis) = conclusion (subjective judgment).

The more data, the more accurate the model is, the more variables, and the clearer the relationship between the data.

Data analysis relies more on business knowledge, data mining focuses more on the implementation of technology, and the requirements for business are slightly reduced, data mining often requires a larger amount of data, and the larger the amount of data, the higher the requirements for technology require stronger programming ability, mathematical ability and machine learning ability. From the perspective of results, data analysis focuses more on the presentation of results, which needs to be interpreted in combination with business knowledge. The result of data mining is a model, through which the law of the entire data is analyzed, and the best for the future is realized at one time, such as judging the characteristics of users and what kind of marketing activities users are suitable for.

Obviously, data mining goes a little deeper than data analysis. Data analytics is a tool that transforms data into information, while data mining is a tool that transforms information into cognition.

Data mining (English: data mining), also translated as data mining, data mining. It is a database knowledge discovery (English:

knowledge-discovery in databases (kdd). Data mining generally refers to the process of automatically searching for information with special relationships hidden in large amounts of data.

Not all information discovery tasks are considered data mining. For example, the use of database management systems to find individual records, or the search engine of the Internet to find specific web pages, are tasks in the field of information retrieval. While these tasks are important and may involve the use of complex algorithms and data structures, they rely heavily on traditional computer science techniques and the distinct features of data to create index structures that effectively organize and retrieve information.

Nonetheless, data mining techniques have also been used to enhance the capabilities of information retrieval systems.

The course not only cultivates students' hard data mining theory and Python data mining algorithm skills, but also takes into account the cultivation of students' soft data governance chain thinking, business strategy optimization thinking, mining business thinking, algorithm thinking, and analytical thinking, so as to improve students' data insight in an all-round way. Click here to book a free trial lesson.

The Concept of Data Mining:

Data mining is a process that uses scientific methods in the fields of mathematics, statistics, artificial intelligence, and machine learning to extract implicit, pre-unknown, and potentially valuable models from a large number of incomplete, noisy, fuzzy, and random data.

The essential difference between data mining and traditional data analysis methods (query, report, statistics, and online analysis and processing (OLAP)) is that data mining excavates information and discovers noisy broadness without explicit assumptions. The pattern obtained by data mining has three characteristics: implicit, unpredictable, and potentially valuable.