What is the relationship between data mining and algorithms?

1. Modeling: Use existing data and models for the language of unknown variables.

Categorical, which is used for discrete target variables.

Regression, which is used for continuous target variables.

3. Correlation analysis (also known as relationship model): reflects the interdependence and correlation between one thing and other things. It is used to discover patterns that describe strongly associated features in data.

4. Anomaly detection: identify observations whose characteristics are significantly different from those of other data.

Sometimes data mining is also divided into: classification, regression, clustering, and association analysis.

Data mining refers to the automated process of categorizing large amounts of data to identify trends and patterns through data analysis and build relationships to solve business problems. In other words, 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 that people do not know in advance.

In principle, data mining can be applied to any type of information repository and transient data (such as data streams), such as databases, data warehouses, dataset imitation rental markets, transaction databases, spatial databases (such as maps, etc.), engineering design data (such as architectural design, etc.), multi-data (text, images, audio), networks, data streams, time series databases, etc. Because of this, data mining has the following characteristics:

1) The dataset is large and incomplete.

The data set required for data mining is very large, and only the larger the data set, the closer the obtained law can be to the correct actual law of the actual liquid he, and the more accurate the result will be. Other than that, the data is often incomplete.

2) Inaccuracy.

There are inaccuracies in data mining, mainly caused by noisy data. For example, in business, users may provide false data; In a factory environment, normal data is often subject to electromagnetic or radiated interference, and the normal value is often violated. These abnormal and absolutely impossible data, called noise, can lead to inaccuracies in data mining.

3) Vague and random.

Data mining is fuzzy and random. Ambiguity here can be associated with inaccuracy. Due to the inaccuracy of the data, the data can only be observed as a whole, or because it involves private information, it is impossible to obtain some specific content, at this time, if you want to do relevant analysis operations, you can only do some analysis in general, and cannot make accurate judgments.

There are two explanations for the randomness of the data, one is that the number obtained is random; We don't know exactly what the user is filling in. The second is that the results of the analysis are random. The data is handed over to the machine for judgment and learning, then all operations are gray box operations.

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The expert system (based on past rules of thumb) and pattern recognition are used to achieve the above goals.