Data mining method of big data system

Abstract

The invention relates to the field of network technology and data processing methods, in particular to a data mining method of a big data system. The method is characterized by including: drawing sample data from a logic database for preprocessing, setting a retrieval sequence, building a retrieval sub-model, calculating input sample data by the retrieval sub-model, and outputting a retrieval result. Compared with the prior art the data mining method has the advantages target data extraction from massive data can be completed fast, efficient optimization and sampling methods are used to allow big data sets to be efficient during storage and calculation mining, and the method is low in complexity, fast in retrieving, and the like.

Description

Technical field: [0001] The invention relates to the field of network technology and data processing methods, in particular to a data mining method of a big data system capable of rapidly extracting target data from massive data. Background technique: [0002] With the high development of information technology, the amount of data accumulated by people is increasing day by day, how to quickly obtain effective data from massive data has become a top priority. Big data refers to data sets that cannot be retrieved and managed using conventional tools under current conditions. It has the characteristics of large data volume, various data types, low value density, and fast processing speed. At present, when searching, users usually hope to quickly obtain what they need from the existing information, which involves a problem of accuracy and speed. The current data retrieval methods such as KD-tree, M-tree, etc. are in the data dimension Good results can be achieved when the dimen...

AI Technical Summary

Problems solved by technology

At present, when searching, users usually hope to quickly obtain what they need from the existing information, which involves a problem of accuracy and speed. The current data retrieval methods such as KD-tree, M-tree, etc. are in the data dimension Good results can be achieved when the dimension is less than 10, but it is not suitable for large data systems

In addition, in the process of network retrieval, more and more users are pursuing to quickly obtain the same or similar results, and the existing retrieval methods are limited in approximate retrieval.

Images