To increase the local search speed,the α-nearness candidate set and don t-look bit techniques are introduced.
首先使用最近α值方法构造初始TSP回路,然后运用混合的局部搜索即2-opt算法、双桥策略和3-opt算法来改进初始回路,并且引进α-nearness候选集和don’t-lookbit技术来提高搜索速度。
In the algorithm the items and the sequence are discussed respectively, and the time join method is used to introduce the candidate sets, so the frequent sets can be gotten.
该算法考虑了项目集与序列之间的关系 ,利用时序连接法 ,采用不同的构造法 ,构造出相对应的候选集 ,从而计算出频繁集。
In light of the relationship between the optimal TSP tours and spanning trees,the minimum spanning 1-tree and a new measurement are introduced into the ant colony algorithm to construct dynamic candidate sets.
利用旅行商问题中最优路径和生成树之间的关系,论文将最小生成1-树的概念引入蚁群算法,并提出一种新的量度来构造动态候选集。
FP growth as a algorithm of mining frequent itemsets,compared with some algorithms for frequent itemsets based Apriori, is characteristic of having no use of many candidate itemsets.
FP-growth算法是一个频繁集产生算法,与一般的类似于Apriori的频繁集产生算法相比,FP-growth的优点在于它不需要产生大量的候选集,因而在时间和空间上都有很好的效率。
Fp-growth algorithm is one of the currently fastest and most popular algorithms for mining association rule without candidate generation.
Fp-growth算法是当前挖掘频繁项目集算法中速度最快,应用最广,并且不需要候选集的一种挖掘关联规则的算法。