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

【摘要】：空間聚類應(yīng)當(dāng)同時滿足空間位置鄰近和屬性相似,在此背景下,為滿足空間鄰近實體之間趨勢性和不均勻性的屬性聚類需求,提出一種基于圖論和信息熵的空間聚類算法。該算法主要是在Delaunay三角網(wǎng)空間位置聚類基礎(chǔ)上,通過引入信息熵,采用多元相似性度量方法以解決二元關(guān)系在屬性聚類中的缺陷,同時基于"等概率最大熵"原則提出了一種局部參數(shù)度量方法,用于表達鄰近目標間屬性分布的局部變化信息。將本文方法與多約束聚類方法和DDBSC聚類方法進行對比分析,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大時,DDBSC和多約束算法會將空間簇內(nèi)的實體誤判為噪聲;(2)在對異常值的敏感性問題上,3類方法都能識別出異常值的位置,但DDBSC和多約束算法對異常值具有一定的敏感性,聚類結(jié)果會掩蓋屬性分布的趨勢性,本文方法受異常值影響很小。通過模擬實驗和實際算例可以發(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:第一,能反映實體屬性在空間分布中的趨勢性特征;第二,能滿足屬性空間分布不均勻;第三,對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)境教育部重點實驗室;江蘇省地理信息資源開發(fā)與利用協(xié)同創(chuàng)新中心;
【基金】：國家自然科學(xué)基金(編號:41671392) 公安部科技強警基礎(chǔ)工作專項項目(編號:2015GABJC39)~~
【分類號】：P208

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