What are the data analysis methods of big data? How to study?

Classification is a basic data analysis method. According to its characteristics, data objects can be divided into different parts and types, and further analysis can further explore the essence of things.

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Regression is a widely used calculation and analysis method. By specifying dependent variables and independent variables to determine the causal relationship between variables, a regression model is established, and the parameters of the model are solved according to the measured data, so as to evaluate whether the regression model can fit the measured data well. If it can be well fitted, further prediction can be made according to the independent variables.

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Clustering is a classification method that divides data into some aggregation classes according to the connotation of data, and the elements in each aggregation class have the same characteristics as much as possible, and the characteristics between different aggregation classes are as different as possible. Unlike classification analysis, the category of classification is unknown. Therefore, cluster analysis is also called unsupervised or unsupervised learning.

4. Similarity matching

Similarity matching is to calculate the similarity of two data by a certain method, and the similarity is usually measured by percentage. Similar matching algorithms are used in many different accounting scenarios, such as data cleaning, user input error correction, recommendation calculation, plagiarism detection system, active scoring system, web search and DNA sequence matching.

5. Frequent itemsets

Frequent itemsets refer to frequent itemsets in cases, such as beer and diapers. Apriori algorithm is a frequent itemset algorithm for mining related rules. Its core idea is to mine frequent itemsets through two stages: candidate set generation and plot decline detection. Now it has been widely used in business, network security and other fields.