Application of data mining in agricultural topic tracking

Abstract

This paper combined characteristics of agricultural topic tracking， researches attributes reduction of Rough Set Theory. The experimental results show that the algorithm is verified to be more feasible and effective. Solve the key problems of agricultural topic tracking.

Integrating agricultural sustainability into policy planning: A geo-referenced framework based on Rough Set theory

“…RST has also been applied in agricultural science as a tool for data reduction. It was applied specifically as an element in multi-stage models for multivariate and complex problem solving, like best partner selection in supply chains (Guo and Lu, 2013), agricultural topic tracking (Zhang et al, 2011), evaluation of soil fertility (Chen and Ma, 2011), and agricultural Big Data management (Shi et al, 2012;Liu et al, 2009). Finally, three relevant applications of RST regard detecting cause-effect links in forecast modeling for rural area depletion and agricultural water demand (Li et al, 2010), and as a knowledge acquisition step in expert system formulations for agricultural problem solving (Li et al, 2013).…”

Influence of Chloride Contamination on the Specific Surface Area of Loess

“…Wen Shuyao, Qiu Zhengsong, Che Shen et al had analyzed the test method to determinate SSA of swelling soil, clay rock, and shale [9][10][11], respectively. Guo Chaohui et al had analyzed the effects of heavy metal pollutants in the soil [12]. Wang Chunyan et al had analyzed the effect of chlorine salt snow-melting agent on soil [13].…”