A data mining model of knowledge discovery based on the deep learning

With the development of the database technology and the spread of the internet, the amount of the data in databases increases at an exponential speed, which yields the difficult problems of “excess data” and “information explosion”, etc. The traditional database technology is restricted in reading and writing, querying and basic statics operations, but can´t acquire the deep data attributes or implicit information. Facing with the huge database in all kinds of fields, it is more and more difficult to cope with the big data only by using conventional technology. New technique to deal with these data at a high level is eagerly demanded. Therefore, the KDD (Knowledge Discovery in Database) technology arises at the historic moment. KDD is an integrated process, which includes data input, iterative solving, user interface and many other custom requirements and design decisions, where the data mining (DM) is a key and specific step in KDD. This paper deeply analyzes state of the art technology of DM, and points out the challenge and technological bottleneck of DM. Moreover, a data mining model architecture of knowledge discovery based on deep learning is proposed.