What are the big data mining methods?

Data mining refers to the process of extracting potentially hidden useful information and knowledge from a large number of incomplete, messy, fuzzy and random data that people don't know in advance. Let's talk about several methods we will use when mining big data:

Method 1. (Visual analysis) Data visualization is the most basic requirement of data analysis tools, whether experts or ordinary users. Visualization can display data intuitively, let the data speak for itself and let the audience see the results.

Method two. (Data mining algorithm) If visualization is for people, then data mining is for machines. Algorithms such as clustering, segmentation and outlier analysis enable us to dig deep into data and value. These algorithms not only deal with a large amount of data, but also reduce the speed of processing big data as much as possible.

Method three. (predictive analysis ability) data mining enables analysts to better understand the data, while predictive analysis enables analysts to make some predictive judgments based on the results of visual analysis and data mining.

Method four. (Semantic Engine) As the diversity of unstructured data brings new challenges to data analysis, a series of tools are needed to parse, extract and analyze data. Semantic engines need to be designed to intelligently extract information from "documents".

Method five. (Data Quality and Master Data Management) Data quality and data management are some management best practices. Processing data through standardized processes and tools can ensure that predefined high-quality analysis results are obtained.

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