本文選題：空間聚類(lèi) + Delaunay三角網(wǎng)�。� 參考：《遙感學(xué)報(bào)》2017年06期

【摘要】：空間聚類(lèi)應(yīng)當(dāng)同時(shí)滿足空間位置鄰近和屬性相似,在此背景下,為滿足空間鄰近實(shí)體之間趨勢(shì)性和不均勻性的屬性聚類(lèi)需求,提出一種基于圖論和信息熵的空間聚類(lèi)算法。該算法主要是在Delaunay三角網(wǎng)空間位置聚類(lèi)基礎(chǔ)上,通過(guò)引入信息熵,采用多元相似性度量方法以解決二元關(guān)系在屬性聚類(lèi)中的缺陷,同時(shí)基于"等概率最大熵"原則提出了一種局部參數(shù)度量方法,用于表達(dá)鄰近目標(biāo)間屬性分布的局部變化信息。將本文方法與多約束聚類(lèi)方法和DDBSC聚類(lèi)方法進(jìn)行對(duì)比分析,結(jié)果表明:(1)在屬性空間分布不均的情況下,本文方法的聚類(lèi)精度要高于多約束方法和DDBSC方法,尤其是當(dāng)屬性空間分布不均程度不斷擴(kuò)大時(shí),DDBSC和多約束算法會(huì)將空間簇內(nèi)的實(shí)體誤判為噪聲;(2)在對(duì)異常值的敏感性問(wèn)題上,3類(lèi)方法都能識(shí)別出異常值的位置,但DDBSC和多約束算法對(duì)異常值具有一定的敏感性,聚類(lèi)結(jié)果會(huì)掩蓋屬性分布的趨勢(shì)性,本文方法受異常值影響很小。通過(guò)模擬實(shí)驗(yàn)和實(shí)際算例可以發(fā)現(xiàn),在保證空間鄰近的基礎(chǔ)上本文方法具有如下優(yōu)勢(shì):第一,能反映實(shí)體屬性在空間分布中的趨勢(shì)性特征;第二,能滿足屬性空間分布不均勻;第三,對(duì)異常值具有良好的穩(wěn)健性。
[Abstract]:Spatial clustering should satisfy both spatial location proximity and attribute similarity. In order to meet the demand of attribute clustering between spatial adjacent entities, a new spatial clustering algorithm based on graph theory and information entropy is proposed. The algorithm is mainly based on Delaunay triangulation spatial location clustering, by introducing information entropy, adopting multivariate similarity measure method to solve the defect of binary relation in attribute clustering. At the same time, based on the principle of "equal probability maximum entropy", a local parameter measurement method is proposed to express the local variation information of attribute distribution between adjacent objects. The results show that: (1) the clustering accuracy of this method is higher than that of multi-constraint method and DDBSC method under the condition of uneven distribution of attributes in space, and the clustering accuracy of this method is higher than that of multi-constraint method and DDBSC method, the results show that: (1) the clustering accuracy of this method is higher than that of multi-constraint method and DDBSC method. Especially, DDBSC and multi-constraint algorithms will misjudge the entities in the spatial cluster as noise when the degree of spatial distribution is increasing. (2) on the sensitivity of outliers, all three methods can identify the position of outliers. However, DDBSC and multi-constraint algorithms are sensitive to outliers, and the clustering results cover up the tendency of attribute distribution. The method in this paper has little effect on the outliers. Through simulation experiments and practical examples, it can be found that the method has the following advantages: firstly, it can reflect the trend characteristics of entity attributes in spatial distribution, second, it can satisfy the non-uniform spatial distribution of attributes, and the method has the following advantages: firstly, it can reflect the trend of the physical attributes in the spatial distribution, second, it can satisfy the non-uniform spatial distribution of the attributes. Thirdly, it has good robustness to outliers.
【作者單位】： 南京師范大學(xué)虛擬地理環(huán)境教育部重點(diǎn)實(shí)驗(yàn)室;江蘇省地理信息資源開(kāi)發(fā)與利用協(xié)同創(chuàng)新中心;
【基金】：國(guó)家自然科學(xué)基金(編號(hào):41671392) 公安部科技強(qiáng)警基礎(chǔ)工作專(zhuān)項(xiàng)項(xiàng)目(編號(hào):2015GABJC39)~~
【分類(lèi)號(hào)】：P208

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