Guide machine learning and data mining are closely related. In order to carry out data mining, it is necessary to master some common knowledge of methods and models used in machine learning, and the best model for processing data can be obtained through model practice. So what are the common models used by big data analysts for data mining? Let's take a look at it.

1, semi-supervised learning

Semi-supervised learning algorithm requires that some of the input data are identified and some are not. This learning model can be used for prediction, but the model needs to learn the internal structure of the data first in order to organize the data reasonably for prediction. The application scenarios include classification and regression, and the algorithms include some extensions of the commonly used supervised learning algorithms. These algorithms first try to model the unmarked data, and then predict the marked data.

2. Unsupervised learning model

In unsupervised learning, the data is not specially identified, and the learning model is to infer some internal structures of the data. The application scenarios include the learning of association rules and clustering.

3. Supervised learning model

Supervised learning model, which is often referred to as classification, is trained by existing training samples (that is, known data and their corresponding outputs) to get an optimal model, and then all inputs are mapped into corresponding outputs by using this model, and the output is simply judged to achieve the purpose of classification, which also has the ability to classify unknown data.

The above are the common models used by big data analysts for data mining. I hope everyone who wants to engage in the data analysis industry can learn quickly. If you want to know more, please continue to pay attention!