multiple time series造句
例句與造句
- research on the structure patterns of the multiple time series
多數(shù)據(jù)流時(shí)間序列中的依賴模式發(fā)現(xiàn)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)多時(shí)間序列間關(guān)聯(lián)模式挖掘研究針對(duì)更有分析價(jià)值的多序列關(guān)聯(lián)模式，進(jìn)一步提出一種新穎的關(guān)聯(lián)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)多時(shí)間序列間關(guān)聯(lián)模式挖掘研究針對(duì)更有分析價(jià)值的多序列關(guān)聯(lián)模式，進(jìn)一步提出一種新穎的關(guān)聯(lián)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個(gè)時(shí)序，則可通過(guò)選擇查看器右側(cè)列表中的相應(yīng)集合來(lái)選擇要在圖表中顯示的時(shí)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
在挖掘算法實(shí)現(xiàn)上，根據(jù)序列特征模式的有序性和重復(fù)性，提出了一種無(wú)須生成大量的候選模式集的互關(guān)聯(lián)后繼樹(shù)挖掘算法。 - It's difficult to find multiple time series in a sentence. 用multiple time series造句挺難的
- ,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
我還用vc成功設(shè)計(jì)了一個(gè)挖掘器，并對(duì)由醫(yī)院門診數(shù)據(jù)流、氣溫變化數(shù)據(jù)流、氣壓變化數(shù)據(jù)流組成的多流時(shí)間序列進(jìn)行了挖掘，證明了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
在挖掘算法實(shí)現(xiàn)上，根據(jù)序列特征模式的有序性和重復(fù)性，提出了一種無(wú)須生成大量的候選模式集的互關(guān)聯(lián)后繼樹(shù)挖掘算法，極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
該方法提出通過(guò)基于重要點(diǎn)分段技術(shù)的分段動(dòng)態(tài)挖掘距離作為相似性度量，既保證了度量的魯棒性，又減少計(jì)算復(fù)雜度；利用各個(gè)分段的抽取六個(gè)主要特征，將時(shí)間序列轉(zhuǎn)化成一種特定的符號(hào)序列，在此基礎(chǔ)上利用海量全文索引結(jié)構(gòu)實(shí)現(xiàn)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
該方法利用allen區(qū)間邏輯關(guān)系來(lái)描述時(shí)間序列模式的關(guān)聯(lián)關(guān)系，避免了傳統(tǒng)方法在關(guān)聯(lián)關(guān)系描述的上非同步性；然后通過(guò)時(shí)間觀測(cè)窗口，來(lái)構(gòu)造出一種包含并行模式和串行模式的特殊形式模式序列；最后，在此基礎(chǔ)上構(gòu)造一種廣義的互關(guān)聯(lián)后繼樹(shù)模型，然后用前面挖掘思路實(shí)現(xiàn)關(guān)聯(lián)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
該方法利用allen區(qū)間邏輯關(guān)系來(lái)描述時(shí)間序列模式的關(guān)聯(lián)關(guān)系，避免了傳統(tǒng)方法在關(guān)聯(lián)關(guān)系描述上的非同步性；然后通過(guò)時(shí)間觀測(cè)窗口，構(gòu)造出?種包含并行模式和串行模式特殊形式的模式序列；最后，在此基礎(chǔ)上構(gòu)造一種廣義的互關(guān)聯(lián)后繼樹(shù)模型，然后用前面挖掘思路實(shí)現(xiàn)關(guān)聯(lián)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
研究多流時(shí)序不能簡(jiǎn)單地將它割裂為單流時(shí)序，因?yàn)檫@樣就割裂了數(shù)據(jù)流事件之間的關(guān)系。雖然msdd能夠發(fā)現(xiàn)多流時(shí)間序列中的依賴模式，但是由于其缺少對(duì)數(shù)據(jù)的初始化、事件之間時(shí)間關(guān)系的表示不直觀、算法執(zhí)行的時(shí)間空間開(kāi)銷很大(o(n~5))、不能夠充分發(fā)現(xiàn)多流時(shí)間序列包含的知識(shí)，它只發(fā)現(xiàn)依賴關(guān)系，因此研究新的，高效，全面的發(fā)現(xiàn)多流時(shí)間序列事件之間關(guān)系的算法成為必要。本文分析了單一和多流時(shí)間序列中的知識(shí)發(fā)現(xiàn)，把多流時(shí)間序列事件內(nèi)部存在的關(guān)系表示為：關(guān)聯(lián)模式、依賴模式、突變模式。 - 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
研究多流時(shí)序不能簡(jiǎn)單地將它割裂為單流時(shí)序，因?yàn)檫@樣就割裂了數(shù)據(jù)流事件之間的關(guān)系。雖然msdd能夠發(fā)現(xiàn)多流時(shí)間序列中的依賴模式，但是由于其缺少對(duì)數(shù)據(jù)的初始化、事件之間時(shí)間關(guān)系的表示不直觀、算法執(zhí)行的時(shí)間空間開(kāi)銷很大(o(n~5))、不能夠充分發(fā)現(xiàn)多流時(shí)間序列包含的知識(shí)，它只發(fā)現(xiàn)依賴關(guān)系，因此研究新的，高效，全面的發(fā)現(xiàn)多流時(shí)間序列事件之間關(guān)系的算法成為必要。本文分析了單一和多流時(shí)間序列中的知識(shí)發(fā)現(xiàn)，把多流時(shí)間序列事件內(nèi)部存在的關(guān)系表示為：關(guān)聯(lián)模式、依賴模式、突變模式。 - 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
該方法提出通過(guò)基于摘要重要點(diǎn)分段技術(shù)的分段動(dòng)態(tài)挖掘距離作為相似性度量，既保證了度量的魯棒性，又減少計(jì)算復(fù)雜度;利用各個(gè)分段的抽取六個(gè)主要特征，將時(shí)間序列轉(zhuǎn)化成一種特定的符號(hào)序列，在此基礎(chǔ)上利用海量全文索引結(jié)構(gòu)實(shí)現(xiàn)了相似性的索引查找。