What is data mining? What about data mining?

Data mining is the analysis of observed data sets (often very large) with the aim of discovering unknown relationships and summarizing data in novel ways that can be understood and valued by the data owner.

The whole process of using computer-based methods, including new technologies, to gain useful knowledge from data is called data mining.

Strictly speaking, data mining is not a completely new field, it is quite a bit of "old wine in a new bottle". The three pillars that make up data mining include research in areas such as statistics, machine learning, and databases, as well as visualization and information science. Data mining includes regression analysis, discriminant analysis, cluster analysis and confidence interval in statistics, decision trees and neural networks in machine learning, association analysis and sequence analysis in databases.

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Data mining is the process of extracting potentially useful information and knowledge that is hidden in a large amount of incomplete, noisy, fuzzy, and random data, which people do not know in advance.

Data Mining Process:

Define the problem: Clearly define the business problem and determine the purpose of the data mining.

Data preparation: Data preparation includes: selecting data in large databases and data warehouse targets to extract the target dataset for data mining; Data preprocessing Perform data reprocessing, including checking the integrity of data and data consistency, denoising, filling in lost domains, deleting invalid data, etc.

Data mining: Select the corresponding algorithm according to the type of data function and the characteristics of the data, and perform data mining on the purified and transformed dataset.

Result analysis: Interpret and evaluate the results of data mining and convert them into knowledge that can be finally understood by users.

The techniques of data mining can be roughly divided into: statistical methods, machine learning methods, neural network methods, and database methods. Statistical methods, which can be subdivided into:

Regression analysis (multiple regression, autoregression, etc.), discriminant analysis (Bayesian discrimination, CBR, genetic algorithm, Bayesian belief network, etc.) Neural network methods can be subdivided into: forward neural network (BP algorithm, etc.), self-organizing neural network (self-organizing feature mapping, competitive learning, etc.).

The database approach is mainly based on visual multidimensional data analysis or OLAP method, and there are also attribute-oriented inductive methods.

1. What is data mining?

Data mining is the use of mathematical, statistical, artificial intelligence and neural network and other scientific methods, such as memory reasoning, cluster analysis, association analysis, decision tree, neural network, genetic algorithm and other technologies, from a large amount of data to mine implicit, previously unknown, potentially valuable relationships, patterns and trends for decision-making, and use these knowledge and rules to establish models for decision support, and provide the best decision support methods, tools and processes.

Data mining integrates various disciplines and technologies and has many functions, and the current main functions are as follows:

1) Classification: According to the attributes and characteristics of the analysis object, establish different groups of classes to describe things. For example:

The banking sector has divided customers into different categories based on previous data, and it is now possible to distinguish new customers who apply for loans based on these in order to adopt the corresponding loan scheme.

2) Clustering: Identify the internal rules of the analysis pair, and divide the objects into several classes according to these rules. For example, applicants are classified as high-risk applicants, medium-risk applicants, and low-risk applicants.

3) Association rules: Correlation is a kind of connection that occurs when something happens to other things. For example, people who buy beer every day are also likely to buy cigarettes, and the proportion can be described by the support and credibility of the association.

4) **: Grasp the law of the development of the object of analysis, and foresee the future trend. For example: judgment on future economic development.

5) Detection of deviation: the description of a small number of extreme special cases of the analysis object, revealing the internal causes. For example:

There are 500 cases of fraud in 1 million transactions in the bank, and in order to operate soundly, the bank must discover the internal factors of these 500 cases and reduce the risk of future operation.

Of course, in addition to the above listed and some other functions such as time series analysis, it should be noted that the various functions of data mining do not exist independently, and are interconnected and play a role in data mining.

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 algorithmically searching for hidden information from large amounts of data.

Data mining is often associated with computer science and is accomplished through many methods such as statistics, analytical processing, intelligence retrieval, machine learning, expert systems (relying on past rules of thumb), and pattern recognition. Wangzhou Technology has its own unique insights and experiences in data analysis and visualization, focusing on the practical application analysis of Adobe data products in the United States.

Data mining is often associated with computer science and is accomplished through a number of methods such as statistics, analytical processing, intelligence retrieval, machine learning, expert systems (relying on past rules of thumb), and pattern recognition.

Data mining is a hot topic in the field of artificial intelligence and databases, and the so-called data mining refers to the non-trivial process of revealing hidden, previously unknown and potentially valuable information from a large number of family or guess data in a database.

Data mining is a kind of decision support process, which is mainly based on artificial intelligence, machine learning, pattern recognition, statistics, databases, visualization technologies and other groups, highly automated analysis of enterprise data, make inductive reasoning, from which to dig out potential patterns, help decision-makers adjust market strategies, reduce risks, and make correct decisions.

Data mining refers to the process of algorithmically searching for hidden information from a large amount of Wakan data. Data mining is often associated with computer science, and Chunqing achieves this through many methods such as statistics, analytical processing, intelligence retrieval, machine learning, expert systems (relying on past rules of thumb), and pattern recall.

Data mining is the process of automatically discovering patterns, associations, trends, and hidden information from large amounts of data. It is an interdisciplinary field that combines statistics, machine learning, artificial intelligence, and database technology. Data mining aims to extract useful knowledge by analyzing and interpreting data and is used for decision support, and strategic planning.

Data mining typically involves the following main steps:

1. Data collection: Collect and obtain data that needs to be analyzed, which can be structured data (such as databases) or unstructured data (such as text, images, or audio).

2. Data preprocessing: clean, integrate, transform and reduce the original data to eliminate noise, deal with missing values, unify data formats, etc., and prepare for subsequent analysis.

3. Feature selection and feature extraction: identify features that are meaningful for analysis and use various algorithms and techniques to extract these features from the raw data.

4. Data mining algorithm selection: Select appropriate data mining algorithms or models according to specific problems, such as clustering, classification, association rules, regression, decision-free policy trees, neural networks, etc.

5. Data pattern discovery: Han blindly uses the selected algorithm to analyze and mine the data to find patterns, trends, associations and anomalies.

6. Model evaluation and interpretation: Evaluate the performance and accuracy of mining models, and interpret analysis results to support business decisions.

Data mining has a wide range of applications in many fields, such as marketing, financial risk analysis, customer relationship management, medical diagnosis, cybersecurity, social analysis, etc. It helps organizations identify valuable information from massive amounts of data to provide better evidence and insight for business decisions.