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Today I would like to introduce to you what are the nine common data analysis methods developed by big data. The content of the article is good. Now I would like to share it with you. Friends who feel in need can understand it. I hope it will be helpful to you. Let's read it along with the editor's ideas.

Data analysis is the process of extracting valuable information from data, which requires all kinds of data processing and classification. Only by mastering the correct data classification method and data processing mode can we get twice the result with half the effort. Here are nine necessary thinking modes of data analysis for data analysts:

1. classification

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

two。 Regress

Regression is a widely used statistical analysis method, which can determine the causal relationship between variables by defining dependent variables and independent variables, establish a regression model, and solve the parameters of the model according to the measured data. then evaluate whether the regression model can fit the measured data well, if it can fit well, it can be further predicted according to the independent variables.

3. Clustering

Clustering is a kind of classification in which the data is divided into some aggregation classes according to the inherent properties of the data, the elements in each cluster have the same characteristics as much as possible, and the differences between different aggregation classes are as big as possible. It is different from classification analysis, and the classified classes are unknown. Therefore, clustering analysis is also called unsupervised or unsupervised learning.

Data clustering is a technology for static data analysis, which is widely used in many fields, including machine learning, data mining, pattern recognition, image analysis and biological information.

4. Similar matching

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

5. Frequent itemsets

Frequent itemsets refer to the sets of items that occur frequently in cases, such as beer and diapers. Apriori algorithm is a frequent itemset algorithm for mining association rules. Its core idea is to mine frequent itemsets through the two stages of candidate set generation and plot downward closed detection, which has been widely used in business, network security and other fields.

6. Statistical description

According to the characteristics of the data, the statistical description uses a certain statistical index and index system to show that the information fed back by the data is the basic processing work of the data analysis. the main methods include: the calculation of average index and variation index, the graphic expression of data distribution pattern and so on.

7. Link prediction

Link prediction is a method to predict the relationship that should exist between data. link prediction can be divided into prediction based on node attributes and prediction based on network structure. link prediction based on attributes between nodes includes analyzing information such as attributes of nodes and the relationship between nodes, and the hidden relationship between nodes is obtained by using methods such as node information knowledge set and node similarity. Compared with the link prediction based on node attributes, the network structure data is easier to obtain. A major point of view in the field of complex networks shows that the characteristics of individuals in the network are not as important as the relationships between individuals. Therefore, link prediction based on network structure has attracted more and more attention.

8. Data compression

Data compression refers to reducing the amount of data to reduce the storage space, improve the efficiency of transmission, storage and processing, or reorganize the data according to certain algorithms without losing useful information. a technical method to reduce data redundancy and storage space. Data compression is divided into lossy compression and lossless compression.

9. Causality analysis

Causal analysis is a method of forecasting by using the causal relationship of the development and change of things, and using causal analysis to forecast the market, mainly using the method of regression analysis, in addition, computational economic models and input output analysis are also commonly used.

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