Description: Apriori[1] is a classic algorithm for frequent itemset mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis. Platform: |
Size: 8192 |
Author:kfnei.dk |
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Description: 关联规则挖掘是指从一个大型的数据库中发现有趣的关联或相互关系，Apriori算法就实现了关联规则挖掘。-Association rule mining is from a large database found interesting associations or mutual relations, Apriori algorithm for mining association rules is realized. Platform: |
Size: 189440 |
Author:xiaofeng |
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Description: Apriori算法C++实现，Apriori算法是一种挖掘关联规则的频繁项集算法，其核心思想是通过候选集生成和情节的向下封闭检测两个阶段来挖掘频繁项集-Apriori algorithm C++ realize, Apriori algorithm is an association rule mining frequent itemsets algorithm, the core idea is the frequent item sets through a two-stage closed down detection candidate set generation and plot to dig Platform: |
Size: 1060864 |
Author:lihaoliang |
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Description: Apriori算法是一种挖掘关联规则的频繁项集算法，其核心思想是通过候选集生成和情节的向下封闭检测两个阶段来挖掘频繁项集。而且算法已经被广泛的应用到商业、网络安全等各个领域。-Apriori algorithm is an association rule mining frequent itemsets algorithm, the core idea is to dig down through the closed itemsets candidate sets generated in two phases to detect and plot. And algorithms have been widely applied to business, network security and other fields. Platform: |
Size: 2577408 |
Author:alan |
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Description: Apriori算法[1]是一种最有影响的挖掘布尔关联规则频繁项集的算法。其核心是基于两阶段频集思想的递推算法。该关联规则在分类上属于单维、单层、布尔关联规则。在这里，所有支持度大于最小支持度的项集称为频繁项集，简称频集。-Apriori algorithm [1] is one of the most influential association rule mining algorithm Boolean frequent item sets. Its core is based on a two-stage frequency set recursive algorithm ideas. The association rules on classification is one-dimensional, single, Boolean association rules. Here, all support is greater than the minimum support itemset is called frequent item sets, referred to as the frequency set.generated in two phases to detect and plot. And algorithms have been widely applied to business, network security and other fields. Platform: |
Size: 43008 |
Author:alan |
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Description: 多层次关联规则挖掘算法：cumulate 可以支持跨层的关联规则挖掘。数据集为T10I4D100K，概念层次树有10个根节点，分三层。-Multi-level association rule mining algorithm: cumulate to support cross-layer association rule mining. Dataset T10I4D100K, has 10 concept hierarchy tree root, divided into three layers. Platform: |
Size: 1027072 |
Author:姜杉 |
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Description: 多最小支持度关联规则挖掘算法，数据集为T10I4D100K,多最小支持度阈值文件为MS-change-Multiple minimum supports association rule mining algorithm, the data set is T10I4D100K, more than the minimum support threshold file for the MS-change Platform: |
Size: 940032 |
Author:姜杉 |
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Description: 支持多最小支持度多层次的关联规则挖掘，数据集为T10I4D100K，多最小支持度阈值为MSchange-Support multiple minimum supports multi-level association rule mining, data set T10I4D100K, more than the minimum support threshold MSchange Platform: |
Size: 1064960 |
Author:姜杉 |
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