CAJ | 학술논문

考虑空间数据分布的复杂性与不连续性，提出了一种点目标聚类方法。算法利用全要素Voronoi图准确识别与表达点目标与线面实体的空间相关性；根据点目标位置分布特征计算面积阈值来控制聚类的粒度，同时以空间尺度变化下面积阈值的恒定作为判断尺度收敛的条件，实现点目标的多尺度划分，时间复杂度为O（nlogn ）。经试验验证，聚类尺度随点目标分布特征自适应收敛，算法无须自定义参数，能够有效地发现受线面目标约束的任意形态点目标集群，对异常值处理稳健。
고필공간수거분포적복잡성여불련속성，제출료일충점목표취류방법。산법이용전요소Voronoi도준학식별여표체점목표여선면실체적공간상관성；근거점목표위치분포특정계산면적역치래공제취류적립도，동시이공간척도변화하면적역치적항정작위판단척도수렴적조건，실현점목표적다척도화분，시간복잡도위O（nlogn ）。경시험험증，취류척도수점목표분포특정자괄응수렴，산법무수자정의삼수，능구유효지발현수선면목표약속적임의형태점목표집군，대이상치처리은건。
Considering the complexity and discontinuity of spatial data distribution ,a clustering algorithm of points was proposed .To accurately identify and express the spati al correl ation among points ,lines and polygons ,a Voronoi di agram that is generated by all spati al features is introduced .According to the distribution characteristics of point’s position ,an area threshold used to control clustering granularity was calculated .Meanwhile ,judging scale convergence by constant area threshold ,the algorithm classifies spatial features based on multi‐scale ,with an O(nlogn ) running time .Results indicate that spatial scale converges self‐adaptively according with distribution of points .Without the custom parameters , the algorithm capable to discover arbitrary shape clusters which be bound by lines and polygons ,and is robust for outli ers .