Mining Frequent Itemsets Using Genetic Algorithm
نویسندگان
چکیده
منابع مشابه
Mining Frequent Itemsets Using Genetic Algorithm
In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the scenario. The major advantage of using GA in the discovery of frequent itemsets is that...
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The aim of this paper is to develop a new mining algorithm to mine all frequent itemsets from a transaction database called the vertical index list (VIL) tree algorithm. The main advantages of the previous algorithms, which are frequent pattern (FP) growth and inverted index structure (IIS) mine, are still useful in a new approach as database scanning only done once, and all frequent itemsets a...
متن کاملMining Frequent Itemsets from Large Data Sets using Genetic Algorithms
Association Rules are the most important tool to discover the relationships among the attributes in a database. The existing Association Rule mining algorithms are applied on binary attributes or discrete attributes, in case of discrete attributes there is a loss of information and these algorithms take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA)...
متن کاملMaximal Frequent Itemsets Mining Using Database Encoding
Frequent itemsets mining is a classic problem in data mining and plays an important role in data mining research for over a decade. However, the mining of the all frequent itemsets will lead to a massive number of itemsets. Fortunately, this problem can be reduced to the mining of maximal frequent itemsets. In this paper, we propose a new method for mining maximal frequent itemsets. Our method ...
متن کاملMining Frequent Itemsets Using Support Constraints
Interesting patterns often occur at varied levels of support. The classic association mining based on a uniform minimum support, such as Apriori, either misses interesting patterns of low support or suuers from the bottleneck of itemset generation. A better solution is to exploit support constraints, which specify what minimum support is required for what itemsets, so that only necessary itemse...
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ژورنال
عنوان ژورنال: International Journal of Artificial Intelligence & Applications
سال: 2010
ISSN: 0976-2191
DOI: 10.5121/ijaia.2010.1411