K-means聚类算法因其算法简单、计算效率高,在机器学习、数据挖掘等多个领域得到了广泛应用。然而,传统K-means算法在初始簇心的选取上存在随机性,这可能导致聚类结果的不稳定性。为了解决这一问题,本研究提出了一种基于空间平移的初始...K-means聚类算法因其算法简单、计算效率高,在机器学习、数据挖掘等多个领域得到了广泛应用。然而,传统K-means算法在初始簇心的选取上存在随机性,这可能导致聚类结果的不稳定性。为了解决这一问题,本研究提出了一种基于空间平移的初始簇心选取算法。该算法首先将包含所有样本集的最小空间通过单位空间以一定步长遍历,在单位空间内统计样本点的密度,以此降低计算量。通过逐一选出密度最高的个点作为初始簇心,从而提高了K-means算法的聚类性能。在UCI的12种数据集上进行的实验表明,与传统的K-means、K-means++等算法相比,改进的算法在迭代次数上有所降低,聚类准确率得到了显著提高。K-means clustering algorithm is an important content in the field of machine learning and is widely used because of its simplicity and efficiency. In order to solve the problem that the initial cluster center selection of traditional K-means algorithm is random, an initial cluster center selection algorithm based on space segmentation is proposed. The minimum space containing all sample sets is divided to calculate the density, and the initial cluster centers with the highest density are selected one by one. The selected cluster centers are replaced by random initial cluster centers for K-means clustering. Twelve datasets were tested separately at UCI. The experimental results show that compared with traditional K-means, K-means++ and other algorithms, the improved algorithm has lower iteration times and higher clustering accuracy.展开更多
针对有效核函数(active kernel function)未知的联合平移不变子空间(Union of Shift-InvariantSubspaces,USI),提出了一种压缩采样模型,基于稀疏重构理论,该采样模型能够有效降低信号的采样率。首先建立一个多脉冲雷达回波信号模型,在...针对有效核函数(active kernel function)未知的联合平移不变子空间(Union of Shift-InvariantSubspaces,USI),提出了一种压缩采样模型,基于稀疏重构理论,该采样模型能够有效降低信号的采样率。首先建立一个多脉冲雷达回波信号模型,在信号的延时-多普勒平面上对延时轴离散化,将回波信号表示为USI信号;然后在根据构建的压缩采样模型降低信号采样率的同时,利用稀疏贝叶斯学习和ESPRIT算法由信号样本值估计出雷达回波信号的延时、多普勒频移和反射系数等参数;最后仿真验证了研究结论的有效性。展开更多
文摘K-means聚类算法因其算法简单、计算效率高,在机器学习、数据挖掘等多个领域得到了广泛应用。然而,传统K-means算法在初始簇心的选取上存在随机性,这可能导致聚类结果的不稳定性。为了解决这一问题,本研究提出了一种基于空间平移的初始簇心选取算法。该算法首先将包含所有样本集的最小空间通过单位空间以一定步长遍历,在单位空间内统计样本点的密度,以此降低计算量。通过逐一选出密度最高的个点作为初始簇心,从而提高了K-means算法的聚类性能。在UCI的12种数据集上进行的实验表明,与传统的K-means、K-means++等算法相比,改进的算法在迭代次数上有所降低,聚类准确率得到了显著提高。K-means clustering algorithm is an important content in the field of machine learning and is widely used because of its simplicity and efficiency. In order to solve the problem that the initial cluster center selection of traditional K-means algorithm is random, an initial cluster center selection algorithm based on space segmentation is proposed. The minimum space containing all sample sets is divided to calculate the density, and the initial cluster centers with the highest density are selected one by one. The selected cluster centers are replaced by random initial cluster centers for K-means clustering. Twelve datasets were tested separately at UCI. The experimental results show that compared with traditional K-means, K-means++ and other algorithms, the improved algorithm has lower iteration times and higher clustering accuracy.
文摘针对有效核函数(active kernel function)未知的联合平移不变子空间(Union of Shift-InvariantSubspaces,USI),提出了一种压缩采样模型,基于稀疏重构理论,该采样模型能够有效降低信号的采样率。首先建立一个多脉冲雷达回波信号模型,在信号的延时-多普勒平面上对延时轴离散化,将回波信号表示为USI信号;然后在根据构建的压缩采样模型降低信号采样率的同时,利用稀疏贝叶斯学习和ESPRIT算法由信号样本值估计出雷达回波信号的延时、多普勒频移和反射系数等参数;最后仿真验证了研究结论的有效性。
