multiple time series造句
例句与造句
- research on the structure patterns of the multiple time series
多数据流时间序列中的依赖模式发现算法研究 - 2 . research of mining relationship patterns in multiple time series an algorithm for discovery frequent patterns in multiple time series will be proposed
2)多时间序列间关联模式挖掘研究针对更有分析价值的多序列关联模式，进一步提出一种新颖的关联模式挖掘方法。 - 2 . research of mining relationship patterns in multiple time series an algorithm for discovery frequent patterns in multiple time series will be proposed
2)多时间序列间关联模式挖掘研究针对更有分析价值的多序列关联模式，进一步提出一种新颖的关联模式挖掘方法。 - if the mining model contains multiple time series, choose the series to display in the chart by selecting the corresponding sets in the list to the right of the viewer
如果挖掘模型包含多个时序，则可通过选择查看器右侧列表中的相应集合来选择要在图表中显示的时序。 - after that, we designed a new data model, called inter-related successive trees irst, to find frequent patterns from multiple time series without generation lots of candidate patterns
在挖掘算法实现上，根据序列特征模式的有序性和重复性，提出了一种无须生成大量的候选模式集的互关联后继树挖掘算法。 - It's difficult to find multiple time series in a sentence. 用multiple time series造句挺难的
- ,in addition, 1 design a data miner by use of vc + +, and it is successful to mine the multiple time series of medical data streams, temperature data streams and air pressure data streams
我还用vc成功设计了一个挖掘器，并对由医院门诊数据流、气温变化数据流、气压变化数据流组成的多流时间序列进行了挖掘，证明了twma是可行。 - after that, we designed a new data model, called inter-related successive trees irst, to find frequent patterns from multiple time series without generation lots of candidate patterns . experiment illustrates that the method is simpler and more flexible, efficient and useful, compared with the previous methods
在挖掘算法实现上，根据序列特征模式的有序性和重复性，提出了一种无须生成大量的候选模式集的互关联后继树挖掘算法，极大地提高了挖掘效率。 - the method first segments time series based on a series of perceptually important points, use segment dynamic time warping distance as measurement, and then time series are converted into meaningful symbol sequences in terms of the segment's features and math categorization . after that, use above index model-irst, to achieve fast similarity retrieval in multiple time series
该方法提出通过基于重要点分段技术的分段动态挖掘距离作为相似性度量，既保证了度量的鲁棒性，又减少计算复杂度；利用各个分段的抽取六个主要特征，将时间序列转化成一种特定的符号序列，在此基础上利用海量全文索引结构实现了相似性的索引查找。 - in this algorithm, firstly the states relationship between in time series is represented to allen temporal logic, then use a sliding windows to examine the order or occur relationship of states and obtain a particularly sequence . on the basis of the sequence, we developed a called girst model to achieve finding the frequent relationship patterns in multiple time series
该方法利用allen区间逻辑关系来描述时间序列模式的关联关系，避免了传统方法在关联关系描述的上非同步性；然后通过时间观测窗口，来构造出一种包含并行模式和串行模式的特殊形式模式序列；最后，在此基础上构造一种广义的互关联后继树模型，然后用前面挖掘思路实现关联模式的挖掘。 - in this algorithm, firstly the states relationship between in time series is represented to allen temporal logic, then use a sliding windows to examine the order or occur relationship of states and obtain a particularly sequence . on the basis of the sequence, we developed a called girst model to achieve finding the frequent relationship patterns in multiple time series . experiments shows, compared with the previous methods, the method is more simple, efficient and more applied value
该方法利用allen区间逻辑关系来描述时间序列模式的关联关系，避免了传统方法在关联关系描述上的非同步性；然后通过时间观测窗口，构造出?种包含并行模式和串行模式特殊形式的模式序列；最后，在此基础上构造一种广义的互关联后继树模型，然后用前面挖掘思路实现关联模式的挖掘。 - we ca n't divide the multiple streams time series into singleness times series simply in the research of multiple streams time series, we'll dissever the relation between the events of the multiple streams . although the msdd can find the dependency relationship of multiple streams, but it have n't the initialization of the events, the express of the time relationship between events is not frank, the cost of the algorithm is expensive ( o ( n5 ) ), i ca n't find much more knowledge in multiple time series, it find the dependency patterns only of the multiple time series, so there need a new more effective, frank, complete algorithm to find the knowledge
研究多流时序不能简单地将它割裂为单流时序，因为这样就割裂了数据流事件之间的关系。虽然msdd能够发现多流时间序列中的依赖模式，但是由于其缺少对数据的初始化、事件之间时间关系的表示不直观、算法执行的时间空间开销很大(o(n~5))、不能够充分发现多流时间序列包含的知识，它只发现依赖关系，因此研究新的，高效，全面的发现多流时间序列事件之间关系的算法成为必要。本文分析了单一和多流时间序列中的知识发现，把多流时间序列事件内部存在的关系表示为：关联模式、依赖模式、突变模式。 - we ca n't divide the multiple streams time series into singleness times series simply in the research of multiple streams time series, we'll dissever the relation between the events of the multiple streams . although the msdd can find the dependency relationship of multiple streams, but it have n't the initialization of the events, the express of the time relationship between events is not frank, the cost of the algorithm is expensive ( o ( n5 ) ), i ca n't find much more knowledge in multiple time series, it find the dependency patterns only of the multiple time series, so there need a new more effective, frank, complete algorithm to find the knowledge
研究多流时序不能简单地将它割裂为单流时序，因为这样就割裂了数据流事件之间的关系。虽然msdd能够发现多流时间序列中的依赖模式，但是由于其缺少对数据的初始化、事件之间时间关系的表示不直观、算法执行的时间空间开销很大(o(n~5))、不能够充分发现多流时间序列包含的知识，它只发现依赖关系，因此研究新的，高效，全面的发现多流时间序列事件之间关系的算法成为必要。本文分析了单一和多流时间序列中的知识发现，把多流时间序列事件内部存在的关系表示为：关联模式、依赖模式、突变模式。 - the method first segments time series based on a series of perceptually important points, use segment dynamic time warping distance as measurement, and then time series are converted into meaningful symbol sequences in terms of the segment's features and math categorization . after that, use above index model-irst, to achieve fast similarity retrieval in multiple time series
该方法提出通过基于摘要重要点分段技术的分段动态挖掘距离作为相似性度量，既保证了度量的鲁棒性，又减少计算复杂度;利用各个分段的抽取六个主要特征，将时间序列转化成一种特定的符号序列，在此基础上利用海量全文索引结构实现了相似性的索引查找。