Big Data Mining Tools The company needs a data mining tool to do data mining

The ethink software developed by the team of Zhongke Big Data Laboratory is great.

Let's recommend a new agile BI tool called DataFocus. It uses natural language analysis and processing, and uses search and question-and-answer interaction, which is more in line with user habits, and uses AI intelligence to assist users in exploring data. Lightweight modeling, direct data connection, flexible interaction, more cost-effective, faster launch, more convenient to use, and greater value.

Based on the data processing technology based on the premise of big data, column storage and in-memory computing support second-level response to terabyte-level data, interactive analysis, and drilling up and down to mine data. Targeting business people with no IT background, of course, data analysts can also use it, and they can focus more on the problem itself, skipping the previous heavy programming process. It does not require IT personnel to conduct prior modeling, and can be flexibly adjusted and automatically modeled during the analysis process, improving the efficiency of analysis and improving the insight and timeliness of enterprise decision-making.

Their official website can apply for a trial, and if you are interested, you can try it.

Rapidminer, R, Weka, Knime, Ggobi, and Orange are all excellent mining tools, and you can choose according to your needs.

Commonly used SPASS SAS, domestic ethink data mining.

Specialized data mining tools provide solutions to problems in a specific field, and fully consider the particularity of data and requirements when it comes to algorithms, and make optimizations. Specific data mining tools can be developed for any field. General data mining tools do not distinguish the meaning of specific data, and use general mining algorithms to deal with common data types.

General data mining tools can do a variety of modes of mining, and what to mine and what to use to mine is up to the user to choose according to his or her own application. At present, there are more R language, SPSS, SAS, Excel**, etc.

There are many data mining and analysis tools on the market, and it is recommended to take the CDA data analyst course. The course is based on scenario-based teaching to mobilize students' practical ability in data mining, in the business scenarios designed by the lecturer, the lecturer constantly raises business problems, and then the students gradually think and operate to solve the problem, so as to help students master the truly excellent data mining ability to solve business problems. Click here to book a free trial lesson.

You can try the Big Data Magic Mirror.

The commonly used software for big data mining is:

In addition to providing excellent data mining capabilities, it also provides capabilities such as data preprocessing and visualization, analysis and statistical modeling, evaluation and deployment.

The abbreviation of r-programming is collectively referred to as r. As a free software for statistical calculation and mapping of programming languages and software environments, it is mainly written in C and Fortran languages, and many modules are written in R, which is a great feature of R.

It supports a wide range of standard data mining tasks, including data preprocessing, collection, classification, regression analysis, visualization, and feature selection, and is versatile enough to be used in a wide range of applications, including data analysis and modeling visualization and algorithms.

It is a powerful open-source tool based on the Python language, if you happen to be a Python developer, when you need to find an open-source data mining tool, Orange must be your first choice.

It is an open-source data analysis, reporting, and synthesis platform that also integrates various machine learning components and data mining through its streamlined concept of modular data. Omen.

If you want to know more about big data mining, we recommend the CDA Data Analysis course, which teaches you the agile algorithm modeling skills that enterprises need. You can learn cutting-edge and practical technologies to explore the charm of data; Teach you to build excellent models with implementable and easy-to-operate data science thinking and technical templates; Focus on strategic analysis techniques and data algorithms such as classification, NLP, deep learning, and feature engineering commonly used by enterprises.