面向Weblog的模糊协同聚类算法

面向Weblog的协同聚类算法具有同时发现用户聚类及与之对应的页面聚类的能力,已成为Weblog数据挖掘的重要研究内容。由于现有的面向Weblog的协同聚类算法大多采用硬划分方法将用户和页面分配到聚类,因此,无法很好地处理聚类边界的问题,即一个用户可能属于多个聚类,从而影响了聚类质量。该文给出了一种面向Weblog的模糊协同聚类FCOW(Fuzzy CO-clustering for Weblog)算法来解决协同聚类算法的边界问题,以提高聚类结果的质量。该算法首先利用矩阵Hadamard积运算发现Weblog中隐含的独立用户模式1={,,K}PA pa pa;其次,依据pa k所对应的页面子集将剩余用户分配到该独立模式中,从而产生协同聚类结果 {k,k}CS CP,k=1,,K;最后计算每个用户和页面与协同聚类之间的模糊隶属度,并以该隶属度作为个性化推荐的依据。实验结果表明,FCOW算法具有获得高质量聚类结果的能力。     

Block clustering with Bernoulli mixture models: Comparison of different approaches

The block or simultaneous clustering problem on a set of objects and a set of variables is embedded in the mixture model. Two algorithms have been developed: block EM as part of the maximum likelihood and fuzzy approaches, and block CEM as part of the classification maximum likelihood approach. A unified framework for obtaining different variants of block EM is proposed. These variants are studied and their performances evaluated in comparison with block CEM, two-way EM and two-way CEM, i.e EM and CEM applied separately to the two sets.     

Effcient Mining of Heterogeneous Star-Structured Data

Many of the real world clustering problems arising in data mining applications are heterogeneous in nature. Heterogeneous co-clustering involves simultaneous clustering of objects of two or more data types. While pairwise co-clustering of two data types has been well studied in the literature, research on high-order heterogeneous co-clustering is still limited. In this paper, we propose a graph theoretical framework for addressing starstructured co-clustering problems in which a central data type is connected to all the other data types. Partitioning this graph leads to co-clustering of all the data types under the constraints of the star-structure. Although, graph partitioning approach has been adopted before to address star-structured heterogeneous complex problems, the main contribution of this work lies in an e cient algorithm that we propose for partitioning the star-structured graph. Computationally, our algorithm is very quick as it requires a simple solution to a sparse system of overdetermined linear equations. Theoretical analysis and extensive experiments performed on toy and real datasets demonstrate the quality, e ciency and stability of the proposed algorithm.     

微合金化对Al-Cu-Mg合金时效行为的影响

以微合金化元素对Al-Cu-Mg合金常规时效析出相的影响为出发点,分析了Ag,Si,Ge,Cd等元素对合金时效行为的影响.结果表明具有:完全改变常规析出相结构,形成新析出相;部分改变常规析出相结构,提高析出相的强化效果;促进析出相析出3种作用.分析了采用微合金化方法提高Al-Cu-Mg合金热强性所存在的问题,并对发展趋势进行了展望.     

非线性相关的失效数据联合聚类分析与预测

为了实现对高性能计算机系统大规模失效数据的自动分析和预测,引入了基于信息论的联合聚类思想提取非线性相关失效数据对象.根据失效特征非线性相关性对失效数据进行归类,并给出用于聚类的失效特征标签的定义,以此为基础提出以互信息熵作为相似性度量的非线性相关失效数据联合聚类算法,并从理论上论述了算法的收敛性和局部最优性.实验结果显...     

A unified framework for web video topic discovery and visualization

Together with the explosive growth of web video in sharing sites like YouTube, automatic topic discovery and visualization have become increasingly important in helping to organize and navigate such large-scale videos. Previous work dealt with the topic discovery and visualization problem separately, and did not take fully into account of the distinctive characteristics of multi-modality and sparsity in web video features. This paper tries to solve web video topic discovery problem with visualization under a single framework, and proposes a Star-structured K-partite Graph based co-clustering and ranking framework, which consists of three stages: (1) firstly, represent the web videos and their multi-model features (e.g., keyword, near-duplicate keyframe, near-duplicate aural frame, etc.) as a Star-structured K-partite Graph; (2) secondly, group videos and their features simultaneously into clusters (topics) and organize the generated clusters as a linked cluster network; (3) finally, rank each type of nodes in the linked cluster network by “popularity” and visualize them as a novel interface to let user interactively browse topics in multi-level scales. Experiments on a YouTube benchmark dataset demonstrate the flexibility and effectiveness of our proposed framework.