• Mining maximum frequent patterns is a key problem in data mining research.

    挖掘最大频繁模式是多种数据挖掘应用中的关键问题。

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  • Frequent patterns mining involves mining transactions, sequences, trees and graphs.

    频繁模式挖掘的研究对象包括事务、序列、树和图。

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  • Mining and updating maximum frequent patterns is a key problem in data mining research.

    挖掘和更新最大频繁模式是多种数据挖掘应用中的关键问题。

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  • At the same time, this algorithm is fast in searching for maximum frequent patterns with less space.

    同时,此算法还能以较少的空间代价快速查找最大频繁模式。

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  • Frequent patterns mining is an important aspect of data mining and includes mining transaction, sequence, tree and graph.

    频繁模式挖掘是数据挖掘领域的一个重要方面,研究内容一般包括事务、序列、树和图。

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  • A new algorithm, constrain-based frequent patterns mining, was developed to provide frequent pattern mining with constraints.

    在频繁模式挖掘过程中能够动态改变约束的算法比较少。

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  • When mining such rules, the support threshold can be ignored, so the frequent and infrequent patterns can be produced together.

    挖掘这种规则时,可以忽略支持度阈值,因此可同时得到频繁模式和非频繁模式。

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  • This paper proposes a fast algorithm DMFP and an updating algorithm IUMFP, which are based on Prefix Tree for mining maximum frequent patterns.

    因此,文章提出了一种最大频繁模式的快速挖掘算法DMFP及更新算法IUMFP。

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  • Mining frequent patterns in transaction databases, time series databases, and many other kinds of databases has been studied popularly in data mining research.

    挖掘事务数据库、时间序列数据库中的频繁模式已经成为数据挖掘中很受关注的研究方向。

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  • The frequent patterns of texture could be mined by the algorithm of association rules mining. The association rules could be combined to represent the texture.

    采用关联规则挖掘算法对图像纹理的频繁模式进行挖掘,通过联合关联规则来表达纹理。

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  • A music style identification system MSC is introduced. The system bases on music melody mining. The system takes MIDI as data source, mines frequent patterns of different styles...

    介绍了一个音乐风格识别系统MSC,系统以MIDI乐曲为数据源提取出乐曲的旋律,对不同风格乐曲的旋律进行了频繁模式的挖掘和对测试乐曲的风格识别。

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  • 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。

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  • This extraction shows the most frequent nouns, verbs, or any specific part-of-speech patterns.

    这种提取显示最常用的名词、动词或任何特定的词性模式。

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  • The results showed that patients with more frequent sleep spindles were better able to tolerate noise; it took higher-decibel sounds to disrupt their sleep patterns.

    实验结果表明,出现更多睡眠纺锤波的实验者能较好地容忍噪声;于是用高分贝噪声干扰他们的睡眠。

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  • By theoretic analysis and experimental tests, the algorithm can be used to discover all types of users frequent access patterns.

    通过理论分析和实验验证,该算法可以有效地发现用户频繁访问模式。

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  • To speed up mining sequential patterns, reducing the time cost is very important during discovering sequential frequent sequence.

    提高序列模式挖掘算法效率的关键在于减少发现频繁序列的时间。

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  • The combination of a decision with a subsequent join is one of the most frequent anti-patterns that we found in business process models.

    decision及其后续join的组合是业务流程模型中最常出现的反模式之一。

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  • According to the complexity of pattern, the mined characteristic patterns could be sorted as frequent item, frequent sequence and frequent sub tree, etc.

    根据特征模式复杂性,可分为频繁项模式、频繁序列模式以及频繁子树模式等。

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  • An active research in data mining area is the discovery of sequential patterns, which finds all frequent sub-sequences in a sequence database.

    数据挖掘领域一个活跃的研究分支就是序列模式的发现,即在序列数据库中找出所有的频繁子序列。

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  • Climate change is altering the patterns of freshwater availability in complex ways that can lead to more frequent and severe droughts.

    气象改变也正在以庞杂的方式转变着活水可用模式,这些方法可引致更频繁和严峻的旱灾。

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  • How to generate candidate frequent sequential pattern and calculate its support is a key problem in mining frequent sequential patterns.

    如何确定候选频繁序列模式以及如何计算它们的支持数是序列模式挖掘中的两个关键问题。

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  • Making use of target frequent access patterns, we can forecast user 'target.

    利用目标频繁访问模式,我们可以预测用户浏览页面的意图。

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  • The paper builds tree-model of RNA molecules and utilizes frequent sub tree mining algorithm to mine common topological patterns among RNA secondary structures.

    本文对RNA分子建立树形模型,利用频繁子树挖掘算法挖掘RNA二级结构中的公共拓扑模式。

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  • This paper presents a efficient algorithm to mine frequent movement patterns, which is the set of the frequent trajectories of moving objects.

    本文提出了一种挖掘频繁运动模式即移动对象运动轨迹中所有频繁轨迹集合的高效算法。

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  • With the increasing demand of massive structured data analysis, mining frequent subgraph patterns from graph datasets has been an attention-deserving field.

    随着对大量结构化数据分析需求的增长,从图集合中挖掘频繁子图模式已经成为数据挖掘领域的研究热点。

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  • To meet this purpose, the CLOSET algorithm was modified to obtain frequent close patterns according to a minimum support and a minimum suspicion degree.

    为此,修改了CLOSET算法来按照最小支持度和最小怀疑度来得到频繁闭模式。

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  • Sequential pattern mining, which discovers frequent subsequences as interesting patterns in a sequence database.

    序列模式挖掘就是发现序列数据库中的频繁子序列作为用户感兴趣的模式。

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  • It gives the definition of the frequent maximum pattern with constraint and develop an algorithm for mining frequent maximum patterns with convertible anti-monotone constraint.

    给出基于约束的频繁最大模式的定义和挖掘基于约束的频繁最大模式算法。

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  • Like China, Australia faces particular risks from climate change - risks from higher temperatures, changing rainfall patterns, more frequent extreme weather and rising sea levels.

    和中国一样,澳大利亚也面临着气候变化带来的风险,比如温度升高、降雨规律的变化、更频繁的极端天气和海平面的升高。

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