| 1. | Paper presentes a new algorithm of mining frequent item sets with negative items
论文提出一种新的挖掘含负项目的频繁项集算法,即基于频繁模式树的算法。 |
| 2. | We develop an simple encoding algorithm so that frequent patterns can be easily analyzed , and compared
我们设计了一个简单的编码算法以便容易地分析和比较频繁模式。 |
| 3. | All urge people to put more and more attentions to the frequent pattern mining in graphs
同时，随着各种新应用的不断推出，人们将注意力逐步向图中的频繁模式的产生问题转移。 |
| 4. | Frequent pattern mining technology in data mining is for mining characteristic patterns with frequent occurrences among data
数据挖掘中的频繁模式挖掘技术专注于发现数据中频繁出现的特征模式。 |
| 5. | When there are a great many of items and transactions in the database , frequent - pattern growth algorithm needs more additional computer memory , which may cause the lack of memory
当数据库中的项目数目较大且事务数量巨大时，频繁模式增长算法内存开销很大，可能导致内存空间不足的现象。 |
| 6. | Next , after near 10 years research and development , the most essential phase in association rules mining , frequent pattern acquirement , and its techniques have been improved dramatically
其次，在经历了近10年的发展以后，关联规则挖掘中至关重要的频繁模式获取技术得到了很大的发展。 |
| 7. | This paper discusses and proposes a new simple algorithm of discovering all the frequent itemsets in relational database by the standard sql . experiments which provee the algorithm is high effective
摘要利用标准sql语言提出了一种在关系数据库中挖掘频繁模式的简易算法。实验证明该算法具有较高的效率。 |
| 8. | We devise an algorithm that antomatically constructs temporal and statistical features according to the semantics of the patterns . in the end , the effectiveness of this method is evaluated in an experiment
我们还设计了一个统计特征构造程序，它根据从审计数据中产生的频繁模式的语法自动地构造临时性的、统计的特征。 |
| 9. | The work in the dissertation is strictly bounded in such field by following the two phases , frequent pattern acquirement and rules generation , to deep into the extended research step by step
本文的工作在关联规则挖掘的范畴以内，根据关联规则的生成的二个主要阶段：频繁模式的获取和关联规则的生成进行了深入的拓展性研究。 |
| 10. | While most of these algorithms are based on apriori line , will generate a huge number of candidate itemsets , need multiple scans over database , and maintain a big hash tree , so the time and space complexity is too high
这些算法大多基于apriori算法，在挖掘频繁模式时需要产生大量候选项集，多次扫描数据库和维护一棵很大的hash树，时空复杂度过高。 |
