用户兴趣迁移模式与个性化服务

提出用户浏览兴趣迁移模式作为关联规则生成的基础的思路;给出了用户频繁访问路径的更新算法,用聚类主题号代替浏览序列的页面号,获得用户浏览兴趣迁移模式,以此得到更优化的个性化服务.     

On constructing an optimal consensus clustering from multiple clusterings

Computing a suitable measure of consensus among several clusterings on the same data is an important problem that arises in several areas such as computational biology and data mining. In this paper, we formalize a set-theoretic model for computing such a similarity measure. Roughly speaking, in this model we have k>1 partitions (clusters) of the same data set each containing the same number of sets and the goal is to align the sets in each partition to minimize a similarity measure. For k=2, a polynomial-time solution was proposed by Gusfield (Information Processing Letters 82 (2002) 159-164). In this paper, we show that the problem is MAX-SNP-hard for k=3 even if each partition in each cluster contains no more than 2 elements and provide a -approximation algorithm for the problem for any k.     

一种基于PSO的模糊聚类算法

在大量的模糊聚类算法中,模糊C均值聚类算法是应用最为广泛的,然而它存在着一些缺点:对初始值敏感,对噪声数据敏感,容易陷入局部最优。针对以上问题,提出了一种基于粒子群优化的模糊聚类算法,利用粒子群强大的全局寻优能力,这种算法克服了模糊C均值聚类算法的缺点,试验证明,这种算法是一种很有潜力的模糊聚类算法。     

基于动态网格的数据流聚类分析

提出的增量式数据流聚类算法DGCDS结合网格和密度技术,能够得到任意形状的聚类,通过改进网格密度的计算方式,解决了现有网格算法中丢失数据空间影响信息的问题,并且实现了关键参数的自适应设置,减小了人工参数对聚类结果的影响。     

基于Ant-Tree算法的短文本聚类研究

短文本由于词频过低,使用常规的聚类算法如K-means效果不理想,难得到可接受的准确度。而最近结合使用生物启发及聚类内部有效性测量改进的方法,能够有效改善短文本的聚类效果。针对短文本聚类,提出了改进Ant-Tree的算法。该算法引入了轮廓系数作为内部效度测量,对K-means算法获得的初始聚类划分计算轮廓系数值,根据各聚簇样本值大小排序,将排序结果应用于Ant-Tree算法的初始化步骤中,使Ant-Tree算法性能得到提高。实验结果表明,该算法准确度超过了其它的算法。     

基于遗传算法与FCSS相结合的模糊球壳聚类算法

模糊球壳聚类(FCSS)算法广泛地应用于模式识别与机器学习等领域。由于其采用基于梯度法和交替寻优策略,对初始化比较敏感,容易陷入局部极值点,从而影响聚类效果。将现代全局优化方法之一的遗传算法(GA)与FCSS算法相结合,得到一种新的球壳聚类算法GA-FCSS。数值实验表明:新方法对球壳形数据有令人满意的聚类效果。     

Adaptive spatial information-theoretic clustering for image segmentation

The incorporation of spatial context into clustering algorithms for image segmentation has recently received a significant amount of attention. Many modified clustering algorithms have been proposed and proven to be effective for image segmentation. In this paper, we propose a different framework for incorporating spatial information with the aim of achieving robust and accurate segmentation in case of mixed noise without using experimentally set parameters based on the original robust information clustering (RIC) algorithm, called adaptive spatial information-theoretic clustering (ASIC) algorithm. The proposed objective function has a new dissimilarity measure, and the weighting factor for neighborhood effect is fully adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge blurring effect. Furthermore, a unique characteristic of the new information segmentation algorithm is that it has the capabilities to eliminate outliers at different stages of the ASIC algorithm. These result in improved segmentation result by identifying and relabeling the outliers in a relatively stronger noisy environment. Comprehensive experiments and a new information-theoretic proof are carried out to illustrate that our new algorithm can consistently improve the segmentation result while effectively handles the edge blurring effect. The experimental results with both synthetic and real images demonstrate that the proposed method is effective and robust to mixed noise and the algorithm outperforms other popular spatial clustering variants.     

Binary-Positive下的并行化CURE算法

当CURE算法在处理不均匀的海量数据时,针对随机抽样不具有代表性的问题,提出了一种健壮的并行化改进算法。该算法使用Binary-Positive算法得到原始数据的有效属性,并利用MapReduce并行框架对有效数据进行层次聚类,从而实现了正确率与效率的一种权衡。实验分析表明,改进后的CURE算法具有更高的执行效率,且聚类效果良好。     

模糊聚类分析在数据挖掘中的应用研究

数据挖掘是从大量数据中用平凡的方法发现有用的知识。聚类分析是数据挖掘的一个重要研究领域,它是按照一定的要求和规律将事物进行分类的一种数学方法。随着模糊数学的兴起,用精确的数学的方法研究模糊问题,人们逐渐将精确和模糊统一起来。论文将模糊数学的模糊理论应用于数据挖掘的聚类分析中,讨论了如何利用样本之间的模糊关系分析样本之间的关联程度,给出了模糊聚类分析在数据挖掘中的应用的主要步骤,以及相应的实例分析和程序设计。     

基于动态聚类的证券业客户细分实证研究

在客户关系管理理论基础上,建立了一个包含13个行业特色指标的证券业客户多维细分模型,并利用聚类分析对国内某知名券商的具体客户信息和交易数据进行了实证研究,有效识别出了具有不同特征以及偏好的客户群,并在此基础上提出了相应的营销策略。