本文關(guān)鍵詞：時(shí)空序列數(shù)據(jù)挖掘中若干關(guān)鍵技術(shù)研究　出處：《中南大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 數(shù)據(jù)挖掘 時(shí)空序列 聚類分析 關(guān)聯(lián)分析 灰色模型

【摘要】：時(shí)空序列數(shù)據(jù)挖掘作為時(shí)空數(shù)據(jù)挖掘的一個(gè)重要分支,是專門(mén)針對(duì)時(shí)空數(shù)據(jù)中時(shí)空序列類型的數(shù)據(jù)進(jìn)行研究。時(shí)空序列數(shù)據(jù)不僅描述了地理對(duì)象或現(xiàn)象存在的空間特征,而且有效地記錄了地理對(duì)象或現(xiàn)象隨時(shí)間的演變狀態(tài),因此對(duì)其研究具有重要的意義。本文回顧了國(guó)內(nèi)外相關(guān)研究成果,結(jié)合現(xiàn)有的空間數(shù)據(jù)挖掘與時(shí)間序列數(shù)據(jù)挖掘理論體系,提出了對(duì)時(shí)空序列數(shù)據(jù)進(jìn)行挖掘,探討了時(shí)空序列數(shù)據(jù)挖掘的主要內(nèi)容與技術(shù)手段,就時(shí)空序列數(shù)據(jù)挖掘的技術(shù)中存在的特定問(wèn)題,提出了相應(yīng)的解決策略。本文主要工作包括： (1)在時(shí)空序列聚類分析研究方向,針對(duì)“時(shí)序相似,空間鄰接”的聚類要求,提出了種子點(diǎn)擴(kuò)散的時(shí)空序列聚類算法,首先選取與空間近鄰時(shí)間序列相似性最高的對(duì)象作為種子,對(duì)種子進(jìn)行標(biāo)記并且將標(biāo)記擴(kuò)散到其空間近鄰,然后選取下一個(gè)種子點(diǎn),進(jìn)行標(biāo)記、擴(kuò)散操作,直到所有的時(shí)空序列依附的實(shí)體都被標(biāo)記,該方法計(jì)算簡(jiǎn)單、效率高并且無(wú)需進(jìn)行參數(shù)的設(shè)定,避免了參數(shù)選取的主觀性。 (2)在時(shí)空序列關(guān)聯(lián)規(guī)則研究方向,針對(duì)“后件已知,前件未知”的關(guān)聯(lián)條件,提出了一種約束條件下事件關(guān)聯(lián)規(guī)則算法,首先在后件目標(biāo)事件已知的條件下,通過(guò)一個(gè)有效時(shí)間窗口來(lái)顧及前件事件間及前件事件與后件目標(biāo)事件在時(shí)間上的滯后因素,然后在計(jì)算前件事件集中,只考慮對(duì)后件目標(biāo)事件有效時(shí)間窗口中的候選前件事件集,而不需要對(duì)整個(gè)事件序列中的頻繁事件集進(jìn)行搜索,避免對(duì)整個(gè)事件序列中的頻繁集計(jì)算,從而降低了算法的復(fù)雜度。 (3)在時(shí)空序列預(yù)測(cè)建模研究方向,針對(duì)GM(1,1)能夠?qū)Α靶颖?貧信息”進(jìn)行建模預(yù)測(cè),而缺乏對(duì)空間自相關(guān)性的考慮,提出了STGM(1,1)建模方法,此方法是結(jié)合空間自相關(guān)特性與灰色理論預(yù)測(cè)模型,空間自相關(guān)性是對(duì)空間對(duì)象或現(xiàn)象在空間上的依賴性描述,因而STGM(1,1)能夠處理具有小樣本的時(shí)空序列數(shù)據(jù)。 最后,總結(jié)了本文的研究成果,并展望了時(shí)空序列數(shù)據(jù)挖掘進(jìn)一步需要研究的工作。
[Abstract]:As an important branch of spatio-temporal data mining, spatio-temporal sequence data mining is an important branch. Spatio-temporal data not only describe the spatial characteristics of geographical objects or phenomena, but also focus on the types of spatio-temporal data. And the evolution of geographical objects or phenomena with time is recorded effectively, so it is of great significance to study them. This paper reviews the relevant research results at home and abroad. Combined with the existing theory system of spatial data mining and time series data mining, this paper puts forward the mining of space-time series data, and discusses the main contents and technical means of spatio-temporal series data mining. In view of the specific problems existing in the technology of spatio-temporal series data mining, the corresponding solutions are put forward. The main work of this paper is as follows: 1) in the research direction of spatio-temporal sequence clustering analysis, aiming at the clustering requirements of "temporal similarity, spatial adjacency", a spatio-temporal sequence clustering algorithm based on seed point diffusion is proposed. Firstly, the object with the highest similarity to the spatial nearest neighbor time series is selected as seed, the seed is labeled and the marker is diffused to its spatial neighbor, and then the next seed point is selected for marking and diffusion operation. Until all the objects attached to the spatio-temporal sequence are marked, the method is simple and efficient, and does not need to set parameters, thus avoiding the subjectivity of parameter selection. 2) in the research direction of temporal and spatial sequence association rules, an event association rule algorithm based on constraint condition is proposed for the association condition of "the posterior part is known, the previous part is unknown". First, under the condition that the target event is known, the lag factor between the former event and the latter object event is taken into account by an effective time window, and then the time lag factor between the former event and the latter object event is considered, and then the time lag factor is considered in the calculation of the previous event set. Only the candidate pre-event set in the effective time window of the latter target event is considered, and the frequent event set in the whole event sequence is not searched to avoid the calculation of the frequent set in the whole event sequence. Thus, the complexity of the algorithm is reduced. 3) in the research direction of prediction modeling of time-space series, aiming at "small sample, poor information" can be modeled and forecasted, STGM(1 is put forward because of the lack of consideration of spatial autocorrelation. 1) Modeling method, which combines spatial autocorrelation characteristics with grey theory prediction model, spatial autocorrelation is the spatial dependent description of spatial objects or phenomena, so STGM(1. 1) capable of processing temporal and spatial sequence data with small samples. Finally, the research results of this paper are summarized, and the further research work of spatiotemporal series data mining is prospected.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：P208

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