The concept of candidate particle list is introduced in the MPS gridless numerical method to generate neighboring particle list matrix,which reduces the CPU time to 1/11 of the case before the introduction.

To increase the local search speed,the α-nearness candidate set and don t-look bit techniques are introduced.

In order to minimize the total query processing cost for a given set of queries under maintenance cost constraint,min/max candidate set transforming algorithm was proposed,in which MACVS(maximum candidate views set) and MICVS(minimum candidate views set) were constructed.

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.

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.

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 algorithm is one of the currently fastest and most popular algorithms for mining association rule without candidate generation.

In the process of mining frequent patterns,Apriori algorithm generates a huge number of candidate itemsets as well as needs multiple scans over database.

In the process of association rules mining,the main factor of influencing the mining efficiency is that a large number of candidate items are came into being.