Crossover: 2014

Chapter 302

Not long ago, the marked data and dark data that Lin Hui had been obsessed with.

Although this kind of thing has great direct value and incidental value.

But it is also very difficult for Lin Hui to carry it.

From a purely technical point of view, there are a lot of troubles.

Lin Hui knows many common data mining methods.

But it seems that it is very difficult to reproduce it now.

Take the big data mining method based on AI and cloud computing that is commonly used to mine data in previous lives.

Judging from the name, this kind of bad street data mining method uses the three most popular concepts in the computer/Internet field in the previous life.

——Artificial intelligence, big data, cloud computing.

Indeed, this method of big data mining based on AI and cloud computing is closely related to the above three.

Follow this method for data mining.

Firstly, it should be applied to a cloud service center that has communication connections with multiple online service terminals.

When using this method for data mining, it is also necessary to obtain the mining evaluation index information of the big data mining business corresponding to the big data service control that can currently be executed by the big data decision information of the online cloud computing project.

When performing data mining, it is necessary to configure the artificial intelligence model in advance.

As for why to configure the artificial intelligence model?

Because only the artificial intelligence model is configured, the index classification of the mining evaluation index information can be realized according to the pre-configured artificial intelligence model.

Only in this way can it be easier to obtain the index classification results.

It is not yet finished to get the index classification results.

On this basis, it is necessary to further divide the index classification results into multiple index classification sets.

Then the corresponding index classification mining features are respectively extracted from the plurality of index classification sets divided into the index classification.

Only in this way can efficient and accurate excavation be realized.

In the future, if you want to achieve efficient big data operation efficiency.

In the process of data mining, the index classification mining features are used to provide certain quantitative data.

It is also used to represent the cluster theme feature corresponding to the cluster theme cluster corresponding to the indicator classification set.

And this needs to determine the mining service mode between each index classification set according to the extracted index classification mining features.

And on this basis, the mining service mode between each index classification set is determined.

This is not counted, and then we need to build the corresponding mining service topology map.

According to the constructed mining service topology map.

In this way, the big data mining process corresponding to each indicator classification set can be determined respectively.

After determining the big data mining process corresponding to each indicator classification set.

According to the big data mining process corresponding to each index classification set and the subject entity relationship with subject category identification among the plurality of index classification sets.

The party can execute the big data mining process corresponding to each index classification set in the index classification result.

The steps above for Lin Hui are quite rough.

In fact, the steps of constructing the corresponding mining service topology map according to the determined mining service patterns among the various indicator classification sets are far more than described in a few sentences.

actually involves:

How to determine the mining service pattern between each index classification set?
How to divide the target index classification sets covered by the same mining service model into a mining service distribution map?
How to narrow the distribution range of the mining service distribution map whose distribution heat map matches the preset thermal characteristics according to the distribution heat map in each mining service distribution map and expand the distribution range of the mining service distribution map whose distribution heat map is smaller than the preset number threshold Get the adjusted distribution map of each mining service?

These problems are not so easy to solve overnight.

(End of this chapter)