数据挖掘技术在商业库存决策支持算法的研究

针对商业库存数据库中存在大量冗余特征和噪声,许多数据挖掘算法对于目标数据的维度非常敏感,随着数据特征的增加,算法的时间空间开销也急剧增加.商业库存决策支持数据挖掘算法利用粗糙集技术对数据作预处理,分析过滤这些冗余的例子,减少了噪声的干扰,减少了训练数据,运用粗糙集的分类算法和浮动搜索算法对浮动搜索算法进行了改进.并用此算法进行了仿真实现,验证了改进后算法的优越性.

Research of commercial database decision-making arithmetic of data mining

Business inventories decision support data mining algorithm is an important issue of data mining research. There are a large number of redundant features and noise in data- base for business inventories, many data mining algorithms to target the dimension data is very sensitive. With the characteristics of the data increase, the algorithm of time and space costs also increased dramatically. Business inventories data mining decision support Algorithm using rough set of technical data for pre-processing, filtering of these redundant example, to reduce the noise interference and the training data, the use of rough set of classification algorithms and floating search algorithm to float an improved search algorithm. Using this algorithm to achieve the simulation shows the superiority of the improved algorithms.