In this paper, we consider the relationship between the binding number and the existence of fractional k-factors of graphs. The binding number of G is defined by Woodall as bind(G)=min{ | NG(X) || X |:∅≠X⊆V(G) }. It ...In this paper, we consider the relationship between the binding number and the existence of fractional k-factors of graphs. The binding number of G is defined by Woodall as bind(G)=min{ | NG(X) || X |:∅≠X⊆V(G) }. It is proved that a graph G has a fractional 1-factor if bind(G)≥1and has a fractional k-factor if bind(G)≥k−1k. Furthermore, it is showed that both results are best possible in some sense.展开更多
A mapping f: X→Y is called weak sequence-covering if whenever {ya} is a sequence in Y converging to y ∈ Y, there exist a subsequence {ynk} and xk∈f^-1(ynk)(k∈N) ,x∈f^-1 (y) such that xk→x. The main results are: ...A mapping f: X→Y is called weak sequence-covering if whenever {ya} is a sequence in Y converging to y ∈ Y, there exist a subsequence {ynk} and xk∈f^-1(ynk)(k∈N) ,x∈f^-1 (y) such that xk→x. The main results are: (1) Y is a sequential, Frechet, strongly Frechet space iff every weak sepuence-covering mapping onto Y is quotient, pseudo-open, countably bi-quotient respectively, (2) weak sequence-covering mapping preserves cs-network and certain k-(cs-)networks, thus some new mapping theorems on k-(cs-)notworks are proved.展开更多
为解决均值漂移聚类算法聚类效果依赖于带宽参数的主观选取,以及处理密度变化大的数据集时聚类结果精确度问题,提出一种基于覆盖树的自适应均值漂移聚类算法MSCT(MeanShift based on Cover-Tree)。构建一个覆盖树数据集,在计算漂移向量...为解决均值漂移聚类算法聚类效果依赖于带宽参数的主观选取,以及处理密度变化大的数据集时聚类结果精确度问题,提出一种基于覆盖树的自适应均值漂移聚类算法MSCT(MeanShift based on Cover-Tree)。构建一个覆盖树数据集,在计算漂移向量过程中结合覆盖树数据集获得新的漂移向量结果KnnShift,在不同数据密度分布的数据集上都能自适应产生带宽参数,所有数据点完成漂移过程后获得聚类结果。实验结果表明,MSCT算法的聚类效果整体上优于MS、DBSCAN等算法。展开更多
文摘In this paper, we consider the relationship between the binding number and the existence of fractional k-factors of graphs. The binding number of G is defined by Woodall as bind(G)=min{ | NG(X) || X |:∅≠X⊆V(G) }. It is proved that a graph G has a fractional 1-factor if bind(G)≥1and has a fractional k-factor if bind(G)≥k−1k. Furthermore, it is showed that both results are best possible in some sense.
文摘A mapping f: X→Y is called weak sequence-covering if whenever {ya} is a sequence in Y converging to y ∈ Y, there exist a subsequence {ynk} and xk∈f^-1(ynk)(k∈N) ,x∈f^-1 (y) such that xk→x. The main results are: (1) Y is a sequential, Frechet, strongly Frechet space iff every weak sepuence-covering mapping onto Y is quotient, pseudo-open, countably bi-quotient respectively, (2) weak sequence-covering mapping preserves cs-network and certain k-(cs-)networks, thus some new mapping theorems on k-(cs-)notworks are proved.
文摘为解决均值漂移聚类算法聚类效果依赖于带宽参数的主观选取,以及处理密度变化大的数据集时聚类结果精确度问题,提出一种基于覆盖树的自适应均值漂移聚类算法MSCT(MeanShift based on Cover-Tree)。构建一个覆盖树数据集,在计算漂移向量过程中结合覆盖树数据集获得新的漂移向量结果KnnShift,在不同数据密度分布的数据集上都能自适应产生带宽参数,所有数据点完成漂移过程后获得聚类结果。实验结果表明,MSCT算法的聚类效果整体上优于MS、DBSCAN等算法。
