There is no doubt that frequent players are strongly incented to use their loyalty CARDS to earn free room nights, food and beverage and a variety of other complimentary items.
毫无疑问，赌场会大力鼓励他们的常客利用会员卡，去赢取免费的酒店间夜、餐饮以及其它不同的奖励。
The second, clustered the data, and then discovered frequent items sets in the result of clustering.
然后对数据先进行聚类，再在聚类结果中发掘频繁项目集；
Due to the irreversibility of random hash mapping, current sketch data structures have to traverse the key address space to find frequent items.
由于随机哈希函数不可逆，目前的概要数据结构不得不遍历关键字地址空间以查找和估计频繁项集。
The mining of frequent items in transactional database is an important task of data mining.
挖掘事务库中的频繁项集是数据挖掘的重要任务之一。
Applying the hierarchical sketch, an algorithm that finds hierarchical frequent items over data streams dynamically and approximately was implemented.
应用该多层概要数据结构，实现了面向数据流的多层频繁项集的动态近似查找算法。
Mining frequent items is a basic task in stream data mining.
频繁项集是挖掘流数据挖掘的基本任务。
Mining most frequent K items in data streams means finding K items whose frequencies are larger than other items in data streams.
数据流最频繁K项挖掘是指在数据流中找出K个项，它们的支持数大于数据流中的其他项。
The compound rules are generated by combination among the frequent antecedent items of atomic rules and the estimation of support and confidence for compound rules.
复合规则通过频繁的原子规则前件项组合和支持度和置信度的估算得到。
Therefore a concept named maximum support is introduced, which is used to bind the frequent items mined, and exclude meaningless association rules.
因此提出了最大支持度的概念，用来约束频繁项集的挖掘，排除没有意义的关联规则同时也提高了挖掘的效率。
Besides, tree structure is extensively adopted in data mining because it doesn't need to generate the frequent items and test them.
此外，由于树结构在挖掘频繁项目时不需要产生频繁项集及对这些频繁项进行测试而被广泛应用于数据挖掘中。
Secondly we mine multidimensional frequent items set and generate association rules.
第二步是求基于多维的频繁项集的算法的实现及关联规则生成。
This algorithm first produces frequent items using previous common association rules mining algorithm, then produces weighted frequent items from the previous frequent items.
该算法首先利用普通的关联规则算法产生频繁集，然后在该频繁集的基础上产生加权频繁集。
On this basis, the algorithm of generating frequent items from transformed data sets is proposed.
在此基础上，给出了在伪装后的数据集上生成频繁项集的挖掘算法。
The result indicates that we can remarkably decrease the candidate items and improve the efficiency of mining frequent pattern when using the interest measure.
分析结果表明，利用规则兴趣度能够大大减小候选项目集的大小，有效提高频繁模式挖掘算法的效率。
Because of frequent trading, people are unable to check the source of each item, the buyers buy goods just under the principle of presumption of public summons for the common items and goodwill.
由于交易的频发性，人们在客观上无力去清查每件商品的来源，购买者仅能根据公示公信之原则推定其为普通物品而善意购买并占有之。
This paper also discusses how to set the optimal minimum support for the common association rules mining algorithm, which can guarantee the frequent items are the weighted frequent items' superset.
同时，给出了最优的最小支持度设定方法，保证了普通关联规则算法所产生的频繁集为加权频繁集的超集。
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。
Many approximation algorithms behave well in frequent items mining, but can not control their memory consumption.
许多近似算法能够有效进行频繁项挖掘，但不能有效控制内存资源消耗。
We need search after the new methods of mining association rules, so as to avoid several bugs of frequent items.
为了避免频集方法的一些缺陷，我们需要探索挖掘关联规则的新方法。
In the algorithm the items and the sequence are discussed respectively, and the time join method is used to introduce the candidate sets, so the frequent sets can be gotten.
该算法考虑了项目集与序列之间的关系，利用时序连接法，采用不同的构造法，构造出相对应的候选集，从而计算出频繁集。
Extraordinary items are some unusual or infrequent transactions or events on contrary to frequent items. According to persistence of earnings, they are the weakest items in persistence.
非常项目是相对于经常项目而言的一些非正常或非经常的交易或事项，依照盈利的持续性原理，它是持续性最弱的项目。
It proposes a new database store structure AFP-Tree for mining frequent patterns, makes recommendations by exploring associations between items, exemplifies the approach on real data.
提出了一个新的数据库存储结构AF P -树，利用它来挖掘频繁模式。然后利用项目之间的相互关联做出推荐。最后举例说明了此推荐系统的处理过程。
A frequent items mining algorithm of stream data (SW-COUNT) was proposed, which used data sampling technique to mine frequent items of data flow under sliding Windows.
提出了一种流数据上的频繁项挖掘算法(SW - COUNT)。该算法通过数据采样技术挖掘滑动窗口下的数据流频繁项。
A frequent items mining algorithm of stream data (SW-COUNT) was proposed, which used data sampling technique to mine frequent items of data flow under sliding Windows.
提出了一种流数据上的频繁项挖掘算法(SW - COUNT)。该算法通过数据采样技术挖掘滑动窗口下的数据流频繁项。
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