挖掘速度 meaning in Chinese
digging speed
Examples
- We present a new approach to mine ooa rules using fp - growth algorithm . our experimental results show that the algorithm is more
实验表明,本文提出的方法相比用apriori算法或用dfree算法进行ooa挖掘速度更快。 - All present user access pattern mining tools have some shortcomings , such as slow mining speed , unsuitable anonymous users , complicated usage , inefficiency , low mobility , much mining limitation and so on
现有的用户访问模式挖掘工具都存在一些缺点，如：挖掘速度慢、不适用匿名用户、使用复杂、效率低、灵活性差、挖掘局限性大等。 - Taditional association rule mining methods lack of focus on the results , and the procedure is slow . those algorithms express the regularities with low level primitive data , and the mining association reules are difficult to understand
传统的关联规则挖掘缺少挖掘的针对性，挖掘速度慢，挖掘结果难于理解，挖掘结果的数量巨大，需要进行大量的筛选以便抽取出有用规则。 - And it can find the same visiting pattern of the different interests - oriented user groups to enhance the quality recommended , by means of adjusting the number of clusters , uapomr system solve the contradiction between mining speed and nicety effectively for recommending in real - time
通过调整聚类的个数， uapomr系统有效地解决了挖掘速度和挖掘准确性之间的矛盾，以达到在线推荐的目的。 - Experiments show that fpmine runs two times as fast as the most recently proposed algorithm fp - growth and saves half the memory ; moreover , our algorithm has a quite good time and space scalability with the number of transactions , and has an excellent performance in dense database mining as well ; spf has excellent time and space efficiency ; fpmine - spf algorithm has a far taster speed in association rules mining than the widely used apriori algorithm and has wonderful scalability . association rules mining always generates too many rules , which makes ii difficult to pick the valuable rules where from
实验表明，在相同数据集上，与fp - growth算法相比，算法fpmine的挖掘速度提高了一倍以上，而所需的存储空间减少了一半；随着数据库规模的增大，算法fpmine具有很好的可伸缩性；对于稠密数据集，本文算法也具有良好的性能； spf算法具有极好的时空效率； fpmine - spf算法挖掘关联规则的速度远快于较长期以来广泛使用的apriori算法，并有相当好的可伸缩性。