Experience with a Hybrid Processor: K-Means Clustering

We discuss hardware/software co-processing on a hybrid processor for a compute- and data-intensive multispectral imaging algorithm, k-means clustering. The experiments are performed on two models of the Altera Excalibur board, the first using the soft IP core 32-bit NIOS 1.1 RISC processor, and the second with the hard IP core ARM processor. In our experiments, we compare performance of the sequential k-means algorithm with three different accelerated versions. We consider granularity and synchronization issues when mapping an algorithm to a hybrid processor. Our results show that speedup of 11.8X is achieved by migrating computation to the Excalibur ARM hardware/software as compared to software only on a Gigahertz Pentium III. Speedup on the Excalibur NIOS is limited by the communication cost of transferring data from external memory through the processor to the customized circuits. This limitation is overcome on the Excalibur ARM, in which dual-port memories, accessible to both the processor and configurable logic, have the biggest performance impact of all the techniques studied.     

Using Self-Similarity to Cluster Large Data Sets

Clustering is a widely used knowledge discovery technique. It helps uncovering structures in data that were not previously known. The clustering of large data sets has received a lot of attention in recent years, however, clustering is a still a challenging task since many published algorithms fail to do well in scaling with the size of the data set and the number of dimensions that describe the points, or in finding arbitrary shapes of clusters, or dealing effectively with the presence of noise. In this paper, we present a new clustering algorithm, based in self-similarity properties of the data sets. Self-similarity is the property of being invariant with respect to the scale used to look at the data set. While fractals are self-similar at every scale used to look at them, many data sets exhibit self-similarity over a range of scales. Self-similarity can be measured using the fractal dimension. The new algorithm which we call Fractal Clustering (FC) places points incrementally in the cluster for which the change in the fractal dimension after adding the point is the least. This is a very natural way of clustering points, since points in the same cluster have a great degree of self-similarity among them (and much less self-similarity with respect to points in other clusters). FC requires one scan of the data, is suspendable at will, providing the best answer possible at that point, and is incremental. We show via experiments that FC effectively deals with large data sets, high-dimensionality and noise and is capable of recognizing clusters of arbitrary shape.     

Analogy-Based Practical Classification Rules for Software Quality Estimation

Software metrics-based quality estimation models can be effective tools for identifying which modules are likely to be fault-prone or not fault-prone. The use of such models prior to system deployment can considerably reduce the likelihood of faults discovered during operations, hence improving system reliability. A software quality classification model is calibrated using metrics from a past release or similar project, and is then applied to modules currently under development. Subsequently, a timely prediction of which modules are likely to have faults can be obtained. However, software quality classification models used in practice may not provide a useful balance between the two misclassification rates, especially when there are very few faulty modules in the system being modeled.This paper presents, in the context of case-based reasoning, two practical classification rules that allow appropriate emphasis on each type of misclassification as per the project requirements. The suggested techniques are especially useful for high-assurance systems where faulty modules are rare. The proposed generalized classification methods emphasize on the costs of misclassifications, and the unbalanced distribution of the faulty program modules. We illustrate the proposed techniques with a case study that consists of software measurements and fault data collected over multiple releases of a large-scale legacy telecommunication system. In addition to investigating the two classification methods, a brief relative comparison of the techniques is also presented. It is indicated that the level of classification accuracy and model-robustness observed for the case study would be beneficial in achieving high software reliability of its subsequent system releases. Similar observations are made from our empirical studies with other case studies.     

模糊聚类在数量型关联规则提取中的应用

关系数据库中数量属性的关联规则挖掘问题是经常要遇到的问题。该文利用改进的FCM进行模糊聚类，主要是解决FCM算法的局部极小问题。利用聚类的结果可以使数量型属性关联规则向类别型属性转换，类别型属性再转化为布尔型属性，这样，便可以从许多关联规则的挖掘方法中找出有意义的规则。     

文本聚类算法的分析与比较

聚类是一种重要的数据挖掘形式。介绍了常用的文本聚类算法 ,从各种聚类算法的适用范围、初始参数的影响、终止条件以及对噪声的敏感性等方面对其进行了分析比较。对聚类算法的应用有一定的指导意义     

甲骨文象形码编码方法研究

甲骨文因字形独特、年代久远,所以一直没能进行有效编码。本文吸取现代编码思想,采用模糊数学模型分析甲骨文的部件(字根)特点,对其进行模糊聚类,并使用32个字符(25个英文字母和7个阿拉伯数字)作为码元,与甲骨文中的500多个字根相对应,实现了一字一码的编码方案。在此基础上,运用信息论中的熵理论,分析了这种编码的效率和科学性,得出甲骨文编码的最佳码长大致接近于3,从而为5000多个甲骨文字进行科学编码提供理论基础。     

具有层次关系目标的显示控制方法

针对具有层次关系目标的特殊性，提出通过推拉镜头实施多分辨率智能显示控制的一种LOD方法．首先模拟人眼摄取信息的特点，提出人眼角分辨率及目标重叠率的概念；然后给出层次聚合和分裂的智能显示控制准则；最后提出LOD智能显示控制的方法，从而使大量目标集显示的同时具有全局综合和局部放大的视觉效果．该方法在某C3I系统中做了实验，并给出了实验结果。     

数据挖掘技术--模糊聚类分析在客户关系管理中的应用研究

企业的竞争重点正在经历着从以产品为中心向以客户为中心的转移，客户关系管理作为一种全新的管理、经营理念，越来越引起商家的重视。在数据仓库中进行数据挖掘正逐渐成为CRM中最核心的部分。本文从CRM中数据分析的不确定性，提出采用模糊聚类的分析方法来分析、预测客户行为。     

一种基于OSI应用层的Web群集负载平衡调序策略研究

本文总结了目前基于L7的Web群集负载平衡调度研究，分析了影响性能的主要因素，在估计Web负载时考虑了请求强度以及Web服务器自身的性能，提出了处理能力异构服务器群集的最小负载调度算法。在算法中还同时考虑了服务器在进入临界状态时性能急剧下降的因素，避免群集进入临界状态．新算法能较为准确地跟踪群集系统的负载，更好地
均衡分配负载。     

计算机软件质量综合评价的灰色聚类模型及其应用

根据灰色系统理论,建立了计算机软件质量综合评价的灰色聚类模型,为定量确评价软件质量提供了有效的方法。