Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
In the data mining research area, discovering frequent item sets is an important issue and key factor for mining association rules. For large datasets, a huge amount of frequent patterns are generated for a low support value, which is a major challenge in frequent pattern mining tasks. A Maximal frequent pattern mining task helps to resolve this problem since a maximal frequent pattern contains information...
The association rule mining algorithm Apriori need to repeatedly scan the transaction database and a lot of I/O loads, moreover it may generate huge candidate sets, the complexity of time and space is relatively high. Aiming at the limitation of the algorithm, an algorithm is proposed for association rule mining based on matching array. The algorithm only needs to scan the database once, screens out...
The main difference of the associative classification algorithms is how to mine frequent item sets, analyze the rules exported and use for classification. This paper presents an associative classification algorithm based on Trie-tree that named CARPT, which remove the frequent items that cannot generate frequent rules directly by adding the count of class labels. And we compress the storage of database...
Genetic Algorithm and Association Rules both are commonly used methods in data mining. In this paper, a brief overview of Genetic Algorithm and Association Rules has been given, and this paper has presented an improved extract method of association rules of genetic algorithm based on their respective advantages and disadvantages. It also did some research on designing encoding methods, structuring...
Genetic algorithm is an important algorithm of association rule mining. However, there is some issues that genetic algorithm easy to lead prematuring convergence and into the plight of local optimum, or convergence too much time and consume a large amount of time to search. For resolving this issues, the paper improves the algorithm through adopting an adaptive mutation rate and improving the methods...
With the advancement of their information technology, many enterprises have accumulated a large amount of business data. We hope to analyze these data on a higher level in order to use then better. The current database systems are unable to find the association rules in data, and cannot predict the developing trend and lack method mining information and knowledge hidden behind the data information...
According to the features of sparse data source while mining association rules, the paper designs a special linked-list unit and two strategies to store data in matrix. A novel algorithm, called SMM (Sparse-Matrix Mining), is proposed to find large item sets from sparse matrix. SMM maps database into a binary sparse matrix and stores compressed data into a linked-list, from which to find large item...
To deal with the problem of too many results returned from a Web database in response to a user query, this paper proposes a novel approach, which takes advantage of the contextual preferences to precompute a few representative orders of tuples and uses them to expeditiously provide ranked answers factoring in the information contained in the query. Contextual preferences take the form that item i...
The problem of mining frequent itemsets plays an essential role in mining association rules, but it is not necessary to mine all frequent itemsets, instead it is sufficient to mine the set of frequent closed itemsets, which is much smaller than the set of all frequent itemsets. In this paper, we present an efficient algorithm, FCI-Miner, for mining all frequent closed itemsets. It based on the improved...
Most existing algorithms for mining frequent closed itemsets have to check whether a newly generated itemset is a frequent closed itemset by using the subset checking technique. To do this, a storing structure is required to keep all known frequent itemsets and candidates. It takes additional processing time and memory space for closure checking. To remedy this problem, an efficient approach called...
An algorithm of association rules mining based on binary has been introduced to solve two problems that how to easily generate candidate frequent itemsets and fast compute support. However the basic notion of presented algorithms in generating candidate itemsets is still similar to Apriori. In some degree the efficiency of these algorithms is very confined, and so this paper proposes two different...
In order to overcome the drawbacks of apriori algorithm for mining frequent itemsets, TIMV (Three-dimensional Itemsets Matrix and Vectors) algorithm was proposed, which used three -dimensional itemsets matrix and vectors, and broke through the bottom-up framework of Apriori. Only needed one pass to scan the database and did not create candidate itemsets, we could gain all the frequent itemsets. Furthermore,...
In order to solve these problems how to easily generate candidate frequent item sets and fast compute support of candidate item sets, an algorithm of association rules mining based on binary has been introduced. However, one presented binary mining algorithm is only suitable for mining some relative short frequent item sets since the way of generating candidate item sets is also similar to apriori,...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.