Knowledge acquisition from forestry machinery patent based on the algorithm for closed weighted pattern mining

Abstract

The application of big data mining can create over a trillion dollars value. Patents contain a great deal of new technologies and new methods which have unique value in the product innovation. In order to improve the effectiveness of big data mining and aid the innovation of products of forestry machinery, the algorithm for closed weighted pattern mining is applied to acquire the function knowledge in the patents of forestry machinery. Compared with the other algorithms for mining patterns, the algorithm is more suitable for the characteristics of patent data. It not only takes into account the importance of different items to reduce the search space effectively, but also avoids achieving excessive uninteresting patterns below the premise that assures quality. The extensive performance study shows that the patterns which are mined by the closed weighted pattern algorithm are more representative and the acquired knowledge has more realistic application significance.