In recent years, data mining has attracted widespread attention from major small and medium-sized enterprises. Why did data mining suddenly become popular? Because in today's big data era, enterprises have a large amount of available data, and the amount is huge, and its value and knowledge cannot be estimate. How to extract valuable information from these massive and disorganized data to help enterprises develop better is one of the problems that many enterprises need to solve urgently.

Data mining is the use of algorithms to find hidden information from massive data. That is, the potentially useful process of extracting information and knowledge from large, incomplete, chaotic, fuzzy, and random practical application data. The main steps of the method are to extract data from the database according to the target of analysis and mining, and then organize the data into a wide table suitable for analysis and mining algorithms through ETL, and then use data mining software to mine.

Conventional data mining software generally only supports small stand-alone data processing. But due to these limitations, traditional data mining usually uses sampling methods to reduce the scale of data analysis, resulting in the accuracy of the analysis results. So we will now summarize, what aspects of data mining still need to focus on breakthroughs.

One, intuitive analysis.

Data visualization is a basic function for general users and data analysis experts. Data visualization allows the data to express itself, allowing users to intuitively experience the results.

Algorithms for data mining.

Data mining is the translation of machine language to humans, and data mining is the native language of machines. Segmentation, aggregation, outlier analysis, and various algorithms allow us to extract value from data. The algorithm must be able to cope with a large amount of massive data, and at the same time have a leasing system.

Third, predictive analysis.

Forecast analysis enables data analysts to make some forward-looking judgments based on image analysis and data mining.

Semantic engine.

Semantic machines need to be designed with sufficient artificial intelligence to actively extract information from data. These include machine translation, sentiment analysis, public opinion analysis, intelligent input, question answering systems, etc.

5. Data quality and data management.

In terms of management, data quality and management is the best practice, standardized process and machine processing data can guarantee advance analysis results.

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