Functions of Data Mining

Data Mining predicts future trends and behaviors to make proactive and knowledge-based decisions. The goal of data mining is to discover hidden and meaningful knowledge from the database, mainly including the following five features. 1. Automatic prediction of trends and behavior data mining automatically searches for predictive information in large databases. problems that require manual analysis in the past can be quickly solved.

Data Mining predicts future trends and behaviors to make proactive and knowledge-based decisions. The goal of data mining is to discover hidden and meaningful knowledge from the database, mainly including the following five features. 1. Automatic prediction of trends and behavior data mining automatically searches for predictive information in large databases. problems that require manual analysis in the past can be quickly solved.

　　

Data Mining predicts future trends and behaviors to make proactive and knowledge-based decisions. The goal of data mining is to discover hidden and meaningful knowledge from the database, mainly including the following five features.

1. Automatically predict trends and Behaviors

Data Mining automatically searches for predictive information in large databases. In the past, problems that require a large amount of manual analysis can now be quickly concluded by the data itself. A typical example is market prediction. Data mining uses promotional data in the past to find the most rewarding users in future investment, other predictable problems include predicting bankruptcy and identifying the groups most likely to respond to a specified event.

2. Association Analysis

Data Association is an important and discoverable knowledge in databases. If there is a regularity between the values of two or more variables, it is called Association. Associations can be divided into simple associations, time series associations, and causal associations. The purpose of association analysis is to find hidden associated networks in the database. Sometimes you do not know the association functions of the data in the database, even if you know it, it is not clear. Therefore, the Association Analysis Rules have credibility.

3. Clustering

The records in the database can be divided into a series of meaningful subsets, that is, clustering. Clustering enhances people's understanding of objective reality and is a prerequisite for conceptual description and Deviation Analysis. Clustering technology mainly includes traditional pattern recognition methods and mathematical taxonomy. In early 1980s, Mchalski proposed the concept clustering technology. The key point is that, when dividing objects, not only the distance between objects is considered, but also the classes that are divided must have some connotation descriptions, this avoids some one-sided nature of traditional technologies.

4. concept description

The concept description is to describe the connotation of a certain object and summarize the relevant features of such objects. Conceptual descriptions are divided into characteristic descriptions and distinctive descriptions. The former describes the common features of certain objects, and the latter describes the differences between different types of objects. Generating a class's characteristic description only involves the commonality of all objects in this class. Many methods are used to generate a distinctive description, such as the decision tree method and genetic algorithm.

5. Deviation Detection

The data in the database often has some exception records. It makes sense to detect these deviations from the database. Deviations include many potential knowledge, such as abnormal instances in classification, exceptions that do not meet the rules, deviations between observed results and model predicted values, and changes in the value over time. The basic method for deviation detection is to find meaningful differences between the observed results and the reference values. Differences between data mining and traditional analysis methods

The essential difference between data mining and traditional data analysis (such as query, report, and online application analysis) is that data mining mines information and discovers knowledge without making clear assumptions. the information obtained from data mining should have three features: first unknown, effective, and practical.

Previously unknown information refers to the information that was not expected in advance. Data Mining aims to discover information or knowledge that cannot be intuitively discovered, or even information or knowledge that violates intuition, the more unexpected the information is, the more valuable it may be. the most typical example in commercial applications is that a chain store uses data mining to discover an astonishing connection between diapers and beer.