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  本文選題：伴隨模式 + 時空軌跡挖掘��； 參考：《南京師范大學(xué)》2015年碩士論文

【摘要】：隨著衛(wèi)星定位技術(shù)、無線通信以及跟蹤檢測設(shè)備的快速發(fā)展,人們能夠方便地以低廉的代價獲得海量時空軌跡數(shù)據(jù)。移動對象的位置、屬性都可能隨著時間的推移而發(fā)生變化,人們不僅需要知道某一對象的屬性和空間信息,更想要了解與該對象相關(guān)事件的來龍去脈,以便對其形成原因作出評估,對未來情況進(jìn)行預(yù)測。時空軌跡數(shù)據(jù)恰能有效地表達(dá)移動對象的這些特性。通過分析各種不同對象的時空軌跡數(shù)據(jù),有助于對人類行為模式、交通物流、動物習(xí)性以及市場營銷等進(jìn)行研究。時空軌跡模式挖掘作為數(shù)據(jù)挖掘的重要內(nèi)容吸引了眾多研究者投身其中。時空軌跡伴隨模式是時空數(shù)據(jù)軌跡模式中重要的組成部分,在挖掘具有相同或相似運動模式的移動對象群體以及研究該移動對象群體中各移動對象之間的親近度等方面有著廣泛的應(yīng)用。本文研究時空軌跡伴隨模式挖掘算法,取得的主要研究成果如下：(1)提出了一種基于網(wǎng)格索引的時空軌跡伴隨模式挖掘算法MAP-G(Mining Adjoint Pattern of spatial-temporal trajectory based on the Grid index)。利用網(wǎng)格索引不僅可以提高算法搜索候選伴隨對象集合的速度,而且可以簡化軌跡數(shù)據(jù),降低計算量,提高算法的執(zhí)行效率。實驗結(jié)果表明該算法不僅比利用DBSCAN算法搜索候選伴隨對象集合的伴隨模式挖掘算法時間效率更高,并且由于MAP-G算法排除了部分不準(zhǔn)確的軌跡模式,因此該算法的結(jié)果也相對更加準(zhǔn)確。(2)提出了一種基于伴隨模式挖掘算法CMC的時空軌跡伴隨模式并行挖掘算法P-CMC(Parallel algorithm based on algorithm CMC)。利用Map/Reduce并行編程模型加以實現(xiàn)。將CMC算法中極其耗時的聚類操作分布到各個計算節(jié)點并行處理,以此達(dá)到提高算法時間效率的目的。實驗表明P-CMC算法的執(zhí)行效率與CMC算法相比有較大提升,而且隨著計算節(jié)點數(shù)量的增加,P-CMC算法的加速比較高,在大數(shù)據(jù)集上顯示出了更大的優(yōu)勢。
[Abstract]:With the rapid development of satellite positioning technology, wireless communication and tracking and detection equipment, people can easily obtain a large amount of space-time trajectory data at a low cost.Moving an object's position and properties can change over time. People need not only to know the properties and spatial information of an object, but also to understand the context of the events associated with that object.In order to evaluate the reasons for its formation and predict the future situation.The spatiotemporal trajectory data can effectively express these characteristics of moving objects.It is helpful to study human behavior patterns, transportation logistics, animal habits and marketing by analyzing the spatiotemporal trajectory data of different objects.As an important part of data mining, spatiotemporal trajectory pattern mining attracts many researchers.Space-time trajectory adjoint pattern is an important component of spatio-temporal data locus pattern.It is widely used in mining moving object groups with the same or similar motion patterns and studying the closeness of moving objects in the moving object groups.In this paper, we study the adjoint pattern mining algorithm of spatio-temporal locus. The main research results are as follows: 1) A spatio-temporal Adjoint Pattern of spatial-temporal trajectory based on the Grid index algorithm based on grid index is proposed.Using grid index can not only improve the speed of searching candidate adjoint object set, but also simplify the track data, reduce the computational cost and improve the efficiency of the algorithm.Experimental results show that the proposed algorithm is not only more efficient than the adjoint pattern mining algorithm using DBSCAN algorithm to search candidate adjoint object sets, but also eliminates some inaccurate trajectory patterns by MAP-G algorithm.Therefore, the result of this algorithm is more accurate. (2) A parallel algorithm P-CMC(Parallel algorithm based on algorithm CMCs based on adjoint pattern mining algorithm (CMC) is proposed.The parallel programming model of Map/Reduce is used to realize it.The extremely time-consuming clustering operations in the CMC algorithm are distributed to each computing node to process in parallel in order to improve the time efficiency of the algorithm.Experiments show that the execution efficiency of P-CMC algorithm is much higher than that of CMC algorithm, and with the increase of the number of nodes, the speedup of P-CMC algorithm is higher, which shows more advantages on big data set.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP311.13

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本文編號：1769924