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21.
随着个性化推荐技术的发展,推荐系统面临着越来越多的挑战。传统的推荐算法通常存在数据稀疏性和推荐精度低等问题。针对以上问题,提出了一种融合时间隐语义填充和子群划分的推荐算法[K]-TLFM(Time Based Latent Factor Model Integrated with [k]-means)。该算法利用融合时间因素的隐语义模型对原始用户物品评分矩阵缺失项进行填充,避免了用全局平均值或者用户/物品平均值补全矩阵带来的误差,有效缓解了数据稀疏性问题,同时融合时间因素有效地刻画了用户偏好随时间的变化;完成评分矩阵缺失项填充后,基于二分[k]-means聚类算法将偏好、兴趣特征相似的对象划分到同一个子群中,在目标用户所属的子群中基于选定的协同过滤算法为用户产生推荐列表,提高了推荐效率和准确性。在MovieLens和Netflix数据集上对该算法的推荐性能进行了对比实验,结果表明该算法具有更高的推荐精度。 相似文献
22.
传统的K-modes算法采用简单的属性匹配方式计算同一属性下不同属性值的距离,并且计算样本距离时令所有属性权重相等。在此基础上,综合考虑有序型分类数据中属性值的顺序关系、无序型分类数据中不同属性值之间的相似性以及各属性之间的关系等,提出一种更加适用于混合型分类数据的改进聚类算法,该算法对无序型分类数据和有序型分类数据采用不同的距离度量,并且用平均熵赋予相应的权重。实验结果表明,改进算法在人工数据集和真实数据集上均有比K-modes算法及其改进算法更好的聚类效果。 相似文献
23.
针对基于位置服务中连续查询情况下,用户自身属性信息很容易被攻击者获取,并通过关联获得用户位置隐私的情况,提出了一种利用粒子群聚类加速相似属性用户寻找,并由相似属性匿名实现用户位置泛化的隐私保护方法。该方法利用位置隐私保护中常用的可信中心服务器,通过对发送到中心服务器中的查询信息进行粒子群属性聚类,在聚类的过程中加速相似属性用户的寻找过程,由相似属性用户完成位置泛化,以此实现位置隐私保护。实验结果证明,这种基于粒子群属性聚类的隐私保护方法具有高于同类算法的隐私保护能力,以及更快的计算处理速度。 相似文献
24.
在苏木精-伊红(HE)染色病理图像中,细胞染色分布的不均匀和各类组织形态的多样性给病理图像的自动分割带来极大挑战。为解决该问题,提出了一种基于自监督学习的病理图像三步层次分割方法,对病理图像中各类组织进行由粗略到精细的全自动逐层分割。首先,根据互信息的计算结果在RGB色彩空间中进行特征选择;其次,采用K -means聚类将图像初步分割为各类组织结构的色彩稳定区域与模糊区域;然后,以色彩稳定区域为训练集采用朴素贝叶斯分类对模糊区域进行进一步分割,得到完整的细胞核、细胞质和胞外间隙这三类组织结构;最后,对细胞核部分进行结合形状和色彩强度的混合分水岭分割得到细胞核间的精确边界,进而量化计算细胞核个数、核占比、核质比等指标。对脑膜瘤HE染色病理图像的分割实验结果表明,所提方法对于染色和细胞形态差异保持较高的鲁棒性,各类组织区域分割误差在5%以内,在细胞核分割精度的对比实验中平均正确率在96%以上,满足临床自动图像分析的要求,其量化结果可以为定量病理分析提供依据。 相似文献
25.
Sreedhar Kollem Katta Rama Linga Reddy Duggirala Srinivasa Rao 《International journal of imaging systems and technology》2020,30(4):1271-1293
Medical image processing is typically performed to diagnose a patient's brain tumor prior to surgery. In this study, a technique in denoising and segmentation was developed to improve medical image processing. The proposed approach employs multiple modules. In the first module, the noisy brain tumor image is transformed into multiple low- and high-pass tetrolet coefficients. In the second module, multiple low-pass tetrolet coefficients are applied through a modified transform-based gamma correction method. Generalized cross-validation is used on multiple high-pass tetrolet coefficients to obtain the best threshold value. In the third module, all enhanced coefficients are applied to the partial differential equation method. In the final module, the denoised image is applied to Atanassov's intuitionistic fuzzy set histon-based fuzzy clustering method with centroid optimization using an elephant herding method. Accordingly, the tumor part is segmented from the nontumor part in the magnetic resonance imaging brain images. The method was assessed in terms of peak signal-to-noise ratio, mean square error, specificity, sensitivity, and accuracy. The experimental results showed that the suggested method is superior to traditional methods. 相似文献
26.
It is a crucial need for a clustering technique to produce high-quality clusters from biomedical and gene expression datasets without requiring any user inputs. Therefore, in this paper we present a clustering technique called KUVClust that produces high-quality clusters when applied on biomedical and gene expression datasets without requiring any user inputs. The KUVClust algorithm uses three concepts namely multivariate kernel density estimation, unique closest neighborhood set and vein-based clustering. Although these concepts are known in the literature, KUVClust combines the concepts in a novel manner to achieve high-quality clustering results. The performance of KUVClust is compared with established clustering techniques on real-world biomedical and gene expression datasets. The comparisons were evaluated in terms of three criteria (purity, entropy, and sum of squared error (SSE)). Experimental results demonstrated the superiority of the proposed technique over the existing techniques for clustering both the low dimensional biomedical and high dimensional gene expressions datasets used in the experiments. 相似文献
27.
Zhijiang Li Yingping Zheng Liqin Cao Lei Jiao Yanfei Zhong Caiyi Zhang 《Color research and application》2020,45(4):656-670
Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results. 相似文献
28.
In this paper, we apply genetic algorithms to the field of electoral studies. Forecasting election results is one of the most exciting and demanding tasks in the area of market research, especially due to the fact that decisions have to be made within seconds on live television. We show that the proposed method outperforms currently applied approaches and thereby provides an argument to tighten the intersection between computer science and social science, especially political science, further. We scrutinize the performance of our algorithm's runtime behavior to evaluate its applicability in the field. Numerical results with real data from a local election in the Austrian province of Styria from 2010 substantiate the applicability of the proposed approach. 相似文献
29.
基于弹载雷达多扩展目标检测的应用需求,在CFAR检测输出的基础上,对检测结果的聚类分析方法进行了论证分析,提出了改进的ISODATA算法。该算法摆脱了常用聚类分析算法对目标个数、聚类初值等先验信息的要求以及孤立点的影响。数字仿真实验验证了新算法的有效性。 相似文献
30.
An organization requires performing readiness-relevant activities to ensure successful implementation of an enterprise resource planning (ERP) system. This paper develops a novel approach to managing these interrelated activities to get ready for implementing an ERP system. The approach enables an organization to evaluate its ERP implementation readiness by assessing the degree to which it can achieve the interrelated readiness relevant activities using fuzzy cognitive maps. Based on the interrelationship degrees among the activities, the approach clusters the activities into manageable groups and prioritizes them. To help work out a readiness improvement plan, scenario analysis is conducted. 相似文献