Neighboring Class Set Research Articles

A Filling Algorithm of Mining Constraint Frequent Neighboring Class Set

In mining constraint frequent neighboring class set, it has been reported that the present algorithm is unsuitable for mining long constraint frequent neighboring class set in spatial database. This paper proposes a filling algorithm of mining constraint frequent neighboring class set, which is suitable for mining any length constraint frequent neighboring class set. In order to improve efficiency of mining algorithm, the algorithm uses computing subset of neighboring class set to generate candidate via top-down strategy. Meanwhile, it uses the method of filling subset to double generate candidate frequent neighboring class set, and it also uses ‘and’ operation of corresponding integer to compute support. The result of experiment indicates that the algorithm is faster and more efficient than other algorithms when mining constraint frequent neighboring class sets.

An Algorithm of Constraint Frequent Neighboring Class Set Mining Based on Downward Search

As present constraint frequent neighboring class set mining algorithms have some redundancy candidate constraint frequent neighboring class set and some repeated computing. And so this paper proposes an algorithm of constraint frequent neighboring class set mining based on downward search, which is suitable for mining long constraint frequent neighboring class set from large spatial database via downward search. The algorithm adopts binary arrangement to turn spatial instance into integer, which is regarded as a spatial transaction, and it uses candidate domain mapping to create constraint frequent neighboring class set via downward search, namely, the algorithm creates candidate domain and uses an integer in the domain to map a candidate, downward search is that this integer of mapping candidate is from maximum to minimum. The method is different from traditional down search or top-down search. The experimental result indicates that the algorithm is more efficient than present constraint frequent neighboring class set mining algorithm when mining long constraint frequent neighboring class set.

An Algorithm of Constraint Frequent Neighboring Class Set Mining Based on Interval Mapping

Since the algorithms of constraint frequent neighboring class set mining based on Apriori has some redundancy candidate constraint frequent neighboring class set and some repeated computing, so its efficiency isn’t improved. Hence, this paper proposes an algorithm of constraint frequent neighboring class set mining based on interval mapping, which may efficiently extract short constraint frequent neighboring class set from large spatial database via up search. The algorithm uses binary weights to change neighboring class set into integer, which is looked on as a spatial transaction, and it uses interval mapping to generate constraint frequent neighboring class set via up search, i.e. the algorithm creates an interval to map a range of generating candidate, up search is mapping candidate from minimum to maximum of the interval. The method is different from traditional up search or down-up search. The experimental result indicates that the algorithm is more efficient than the constraint frequent neighboring class set mining algorithm based on Apriori when mining short constraint frequent neighboring class set.

An Algorithm of Constraint Frequent Neighboring Class Set Mining Based on Filling Class Set

Since the algorithms of constraint frequent neighboring class set mining based on Apriori is unsuitable for mining any length constraint frequent neighboring class set and has some redundancy computing, this paper proposes an algorithm of constraint frequent neighboring class set mining based on filling class set, which may efficiently extract any length constraint frequent neighboring class set from large spatial database. The algorithm uses binary conversion to turn neighboring class set into integer, and regards these integers as mining spatial database, and it uses double search strategy to generate constraint frequent neighboring class set, namely, one is that the algorithm uses two k-constraint frequent neighboring class sets to connect (k+1)-candidate constraint frequent neighboring class set, the other is that it also uses filling virtual class set of (k+1)-candidate constraint frequent neighboring class set to generate another candidate. In whole mining course the algorithm need only scan database once. The experimental result indicates that the algorithm is more efficient than the constraint frequent neighboring class set mining algorithm based on Apriori when mining any length constraint frequent neighboring class set.

An Algorithm of Synchronous Mining Frequent Neighboring Class Set with Constraint Class Set

Due to these reasons that present frequent neighboring class set mining algorithms are unsuitable for extracting any length frequent neighboring class set with constraint class set, this paper proposes an algorithm of synchronous mining frequent neighboring class set with constraint class set, which is suitable for mining any length frequent neighboring class set with constraint class set in large spatial database. The algorithm creates mining database through digitization method, and then gains candidate frequent neighboring class set with constraint class set via synchronous search strategy, namely, it uses computing (k-1)-subset of k-non frequent neighboring class set with constraint class set to generate candidate frequent neighboring class set, meanwhile, it also uses connecting (l+1)-superset of l-frequent neighboring class sets to generate candidate frequent neighboring class set with constraint class set, it only need scan database once to extract frequent neighboring class set with constraint class set. The result of experiment indicates that the algorithm is faster and more efficient than present algorithms when mining any length frequent neighboring class set with constraint class set.

An Algorithm of Frequent Neighboring Class Set Mining with Constraint Class

In this paper, the purpose is to find a method that can be more suited to facial expression change and also improve the recognition rate. The proposed system contains three parts, wavelet transform, Fisher linear discriminant method feature extraction and face classification. The basic idea of the proposed method is that first extract the low-frequency components through wavelet transform, then the low-frequency images mapped into a low-dimensional space by PCA transform, and finally the utilization of LDA feature extraction method in low-dimensional space. The algorithms were tested on ORL and Yale face database, respectively. Experimental results shows that the proposed method not only improve the recognition rate, but also improve the recognition speed. This method can effectively overcome the impact of expression changes on face recognition, and play a certain role in inhibition of expression.

“An Algorithm of Fast Duplex Mining Frequent Neighboring Class Set”

“An Algorithm of Fast Duplex Mining Frequent Neighboring Class Set”

“An Algorithm of Complement Mining Frequent Neighboring Class Set”

“An Algorithm of Complement Mining Frequent Neighboring Class Set”