科技通报
科技通報
과기통보
BULLETIN OF SCIENCE AND TECHNOLOGY
2014年
6期
152-154
,共3页
聚类%边界对象%ε-邻域%最小外包矩形
聚類%邊界對象%ε-鄰域%最小外包矩形
취류%변계대상%ε-린역%최소외포구형
cluster%boundary object%epsilon-neighborhood%minimum bounding rectangle
为了实现对提取边界后剩余数据对象的聚类，提出一种由图像边缘出发进行聚类的算法。该算法首先采用深度优先搜索的策略将已知的边界对象进行分类，并计算各边界曲线的最小外包矩形区域；然后运用夹角和法去除内边界类；最后依据近邻原则对每一个核心对象进行归类。实验结果表明，对于含有噪声、密度均匀的数据集，算法可以识别出各种形状的聚类，且聚类质量和时间性能较好。
為瞭實現對提取邊界後剩餘數據對象的聚類，提齣一種由圖像邊緣齣髮進行聚類的算法。該算法首先採用深度優先搜索的策略將已知的邊界對象進行分類，併計算各邊界麯線的最小外包矩形區域；然後運用夾角和法去除內邊界類；最後依據近鄰原則對每一箇覈心對象進行歸類。實驗結果錶明，對于含有譟聲、密度均勻的數據集，算法可以識彆齣各種形狀的聚類，且聚類質量和時間性能較好。
위료실현대제취변계후잉여수거대상적취류，제출일충유도상변연출발진행취류적산법。해산법수선채용심도우선수색적책략장이지적변계대상진행분류，병계산각변계곡선적최소외포구형구역；연후운용협각화법거제내변계류；최후의거근린원칙대매일개핵심대상진행귀류。실험결과표명，대우함유조성、밀도균균적수거집，산법가이식별출각충형상적취류，차취류질량화시간성능교호。
In order to cluster the remaining data objects after extraction of the border, a kind of clustering algorithm based on the minimum bounding rectangle of border is proposed. Firstly, it classify the known boundary object in accordance with the depth-first search, and calculate the minimum bounding rectangle area of every boundary curve. And then exclude from the inner boundary of cluster using the method of the sum of the angle. Finally, cluster the core object cluster by neighbor principle. The experimental results show that, for containing noise, density data set, the algorithm can identify various shapes cluster, and clustering quality and time performance is better.
