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In the course of mining frequent neighboring class set, present algorithms have some redundant candidate and repetitive computing, which are only able to efficiently extract short frequent neighboring class set, and so this paper proposes an algorithm of fast duplex mining frequent neighboring class set, which is suitable for mining any frequent neighboring class set. This algorithm adopts two methods...
As large session pattern are saved in Web server log, aiming at the character that these pages attribute is Boolean quantity, this paper proposes an algorithm of Web usage mining based on attributes number set, which is suitable for mining session pattern containing many visiting pages. The algorithm turns session pattern of users into binary, and then uses the way of number descending to generate...
This paper proposes an algorithm of mining spatial topology association rules with constraint condition based on Apriori, which is used to mining spatial multilayer transverse association rules with constraint condition from large spatial database. This algorithm generates candidate frequent topological item sets via up search strategy similar to Apriori, which is suitable for mining short spatial...
As present frequent neighboring class set mining algorithms inefficiently extract long frequent neighboring class set, and so this paper introduces an algorithm of fast mining long frequent neighboring class set. To fast search long frequent neighboring class set in large spatial data, this algorithm uses down search strategy to generate candidate frequent neighboring class set. But the course of...
this paper proposes an algorithm of spatial association rules mining based on orientation frequent, which is suitable for mining transverse spatial association. The algorithm uses the method of orientation frequent item set to generate frequent candidate items by down-up strategy, which is able to efficiently reduce repeating computing and redundant operation to improve the efficiency of algorithm...
This paper focuses on character of present frequent neighboring class set mining algorithms which is suitable for mining short frequent neighboring class set, and introduces a top-down algorithm in frequent neighboring class set mining. This algorithm is suitable for mining long frequent neighboring class set in large spatial data according to top-down strategy, and it creates digital database of...
This paper introduces an algorithm of mining spatial topology association rules based on Apriori, which is used to mining spatial multilayer transverse association rules from spatial database. This algorithm creates candidate frequent topological itemsets via down-top search strategy as Apriori, which is suitable for mining short spatial topological frequent itemsets. This algorithm compresses a kind...
This paper addresses the existing problems that present frequent neighboring class set mining algorithms is inefficient to extract long frequent neighboring class set in spatial data mining, and introduces a double search mining algorithm in frequent neighboring class set, which is suitable for mining any frequent neighboring class set in large spatial data through down-top search strategy and top-down...
In order to fast generate candidate frequent itemsets, avoid redundant calculation and reduce the time of scanning database, this paper proposes an algorithm of mining frequent itemsets based on digit sequence, which is suitable for mining long frequent itemsets. The algorithm firstly turns all transactions into digital transactions by binary, and then computing digit sequence of every attribute item...
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