聚类算法的分析与研究

本文对聚类分析中聚类算法的基本理论进行了详细分析研究,并在此基础上,提出了一个聚类算法的通用算法框架。     

聚类分析技术在教学指导中的应用

在深入研究数据挖掘聚类分析算法的基础上,针对传统算法的优缺点,提出一种改进的聚类分析算法——GBKM算法,通过理论分析以及实验证明该算法的有效性和先进性,并将其运用到教学实践中。使用该算法对国家计算机二级模拟考试成绩进行分析,根据分析结果获得学员个性特征,为学员提供有针对性的、个性化的学习指导,在教学实践中具有积极的指导意义。     

Towards effective document clustering: A constrained K-means based approach

Document clustering is an important tool for document collection organization and browsing. In real applications, some limited knowledge about cluster membership of a small number of documents is often available, such as some pairs of documents belonging to the same cluster. This kind of prior knowledge can be served as constraints for the clustering process. We integrate the constraints into the trace formulation of the sum of square Euclidean distance function of K-means. Then,the combined criterion function is transformed into trace maximization, which is further optimized by eigen-decomposition. Our experimental evaluation shows that the proposed semi-supervised clustering method can achieve better performance, compared to three existing methods.     

Experience, experimentation, and the accumulation of knowledge: the evolution of R&D in the semiconductor industry

This paper examines the interaction and impact on performance of two organizational mechanisms supporting generation and retention of knowledge: experience accumulation and experimentation. It provides evidence that generation of knowledge through experimentation and retention of knowledge through experience were significantly correlated with performance, whereas other measures of R&D commitment and organization, including several suggested by prior studies, were not. It argues that experience and experimentation can function as at least partial substitutes for one another, possessing more of one, firms may need less of the other, and provides evidence that they were so in the semiconductor industry. The evolution of practice at one firm—Intel Corporation—is explored at greater length, providing a qualitative perspective on the process by which a shift towards greater experimentation occurred.     

A split-list approach for relevance feedback in information retrieval

In this paper we present a new algorithm for relevance feedback (RF) in information retrieval. Unlike conventional RF algorithms which use the top ranked documents for feedback, our proposed algorithm is a kind of active feedback algorithm which actively chooses documents for the user to judge. The objectives are (a) to increase the number of judged relevant documents and (b) to increase the diversity of judged documents during the RF process. The algorithm uses document-contexts by splitting the retrieval list into sub-lists according to the query term patterns that exist in the top ranked documents. Query term patterns include a single query term, a pair of query terms that occur in a phrase and query terms that occur in proximity. The algorithm is an iterative algorithm which takes one document for feedback in each of the iterations. We experiment with the algorithm using the TREC-6, -7, -8, -2005 and GOV2 data collections and we simulate user feedback using the TREC relevance judgements. From the experimental results, we show that our proposed split-list algorithm is better than the conventional RF algorithm and that our algorithm is more reliable than a similar algorithm using maximal marginal relevance.     

基于遗传算法实现网上快速收集"灰色文献"的方法

本文对“灰色文献”归属权离散、时空离散、价值离散等特点及分布现状进行了系统地分析.进而提出应用遗传算法进行编程,达到快速有效的Internet定位,实现读者快速找到特定的“灰色文献”信息地址,获取相关的“灰色文献”信息.     

Evaluating subtopic retrieval methods: Clustering versus diversification of search results

To address the inability of current ranking systems to support subtopic retrieval, two main post-processing techniques of search results have been investigated: clustering and diversification. In this paper we present a comparative study of their performance, using a set of complementary evaluation measures that can be applied to both partitions and ranked lists, and two specialized test collections focusing on broad and ambiguous queries, respectively. The main finding of our experiments is that diversification of top hits is more useful for quick coverage of distinct subtopics whereas clustering is better for full retrieval of single subtopics, with a better balance in performance achieved through generating multiple subsets of diverse search results. We also found that there is little scope for improvement over the search engine baseline unless we are interested in strict full-subtopic retrieval, and that search results clustering methods do not perform well on queries with low divergence subtopics, mainly due to the difficulty of generating discriminative cluster labels.     

无线传感器网络分簇路由协议的对比分析

本文分析了无线传感器网络中基于分簇的路由协议的优点,介绍了几种基于分簇的路由协议,通过分析对比的方式总结了这几种协议在网络性能方面的优缺点。     

基于蚁堆原理的聚类算法在数据挖掘中的应用研究

基于蚁堆原理的聚类算法是一种新的仿生算法,该算法已经在当前的数据挖掘研究中得到了多种应用。本文在介绍回顾该算法发展历史的基础上,简要评述了部分具有代表性的该算法改进模型及其应用情况。最后,对该算法在今后的研究方向作了展望。     

The geometry of linearly and quadratically constrained optimization problems for signal processing and communications

Constrained minimization problems considered in this paper arise in the design of beamformers for radar, sonar, and wireless communications, and in the design of precoders and equalizers for digital communications. The problem is to minimize a quadratic form under a set of linear or quadratic constraints. We present solutions to these problems and establish a connection between them. A majorization result for matrix trace and Poincare's separation theorem play key roles in establishing the connection. We show that our solutions can be formulated as generalized sidelobe cancellers (GSCs), which tie our constrained minimizations to linear minimum mean-squared error (LMMSE) estimations. We then express our solutions in terms of oblique projection matrices and establish the geometry of our constrained minimizations.     

试论数据挖掘中的遗传算法

本文对数据挖掘进行了概述，阐明了数据挖掘的概念、数据挖掘在商业中的意义。如何进行数据挖掘及数据挖掘流程，提出了数据挖掘技术中的一个重要算法-遗传算法的基本原理以及遗传算法在数据挖掘技术中的地位。提出了基于遗传算法的关联规则的提取方法。文章还给出了用遗传算法进行关联规则挖掘的实例，并讨论了数据挖掘技术所面临的问题和挑战。     

无线自组网络路由算法综述

无线Mesh网络路由协议依据路由生成的时间来划分,可分为先验式路由协议、反应式路由协议和混合式路由协议。对无线Mesh网络的有关路由算法进行了研究,主要对先验式路由算法和反应式路由算法的优缺点进行了分析和研究,提出在区域内采用先验式路由,在区域间采用反应式路由。     

The impact of virtual simulation tools on problem-solving and new product development organization

New product development nowadays makes heavy use of IT instruments such as virtual simulation tools. The main motivation for introducing virtual simulation tools in new product development is to speed up development and lower its cost. Virtual simulation tools, however, do much more. They introduce profound changes in the organization, including the nature of problem-solving, bearing the potential to increase new product development performance beyond cost and lead time reduction. Understanding these profound changes, we argue, holds the key to unlocking the potential of virtual simulation tools for improving new product development performance, including more innovative products. We support our argument with a case study from the European auto industry.     

Parallel computation approaches for flexible multibody dynamics simulations

Finite element based formulations for flexible multibody systems are becoming increasingly popular and as the complexity of the configurations to be treated increases, so does the computational cost. It seems natural to investigate the applicability of parallel processing to this type of problems; domain decomposition techniques have been used extensively for this purpose. In this approach, the computational domain is divided into non-overlapping sub-domains, and the continuity of the displacement field across sub-domain boundaries is enforced via the Lagrange multiplier technique. In the finite element literature, this approach is presented as a mathematical algorithm that enables parallel processing. In this paper, the divided system is viewed as a flexible multibody system, and the sub-domains are connected by kinematic constraints. Consequently, all the techniques applicable to the enforcement of constraints in multibody systems become applicable to the present problem. In particular, it is shown that a combination of the localized Lagrange multiplier technique with the augmented Lagrange formulation leads to interesting solution strategies.     

互动效应与中小企业集群化——论中西部区域经济与中小企业发展问题

中小企业集群是当前世界经济中颇具特色的产业组织形式,不仅对于中小企业获取规模经济优势具有积极影响,还有助于区域经济的全面发展。同时,区域经济发展对区域内中小企业发展又能发挥巨大的影响力和推动力。本文主要研究了区域经济发展与集群化的互动效应,以及中国东、中、西部区域经济发展水平与中小企业发展程度差异问题,进而,提出发展中、西部区域中小企业集群化的必要性与发展思路。本文研究不仅对于宏观部门有重点地发展区域经济具有借鉴意义,而且对于解决后转轨时期中西部区域中小企业发展问题具有重要的参考价值。     

报文摘要加密技术在网络化考试中的应用

通过采用网络安全技术中经常使用的“报文摘要”算法．将考生的试卷内容和考生的考号进行摘要计算，然后生成一个固定长度的摘要．而考生试卷中的任何一个字节变化都会影响到该摘要，从而保证考卷的数据完整性。因此．当答卷被修改，或者考生对其答案是否被修改提出质疑时。系统会提供明确的证据。     

并行程序加速比计算和分析

本文通过设计一种简单的并行计算闰年数的程序,计算了双机并行执行的加速比,并运用扩展的Amdahl定律分析了相关实验数据。     

Automatic ranking of information retrieval systems using data fusion

Measuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic environments. In this study, we employ new methods for automatic ranking of retrieval systems. In these methods, we merge the retrieval results of multiple systems using various data fusion algorithms, use the top-ranked documents in the merged result as the “(pseudo) relevant documents,” and employ these documents to evaluate and rank the systems. Experiments using Text REtrieval Conference (TREC) data provide statistically significant strong correlations with human-based assessments of the same systems. We hypothesize that the selection of systems that would return documents different from the majority could eliminate the ordinary systems from data fusion and provide better discrimination among the documents and systems. This could improve the effectiveness of automatic ranking. Based on this intuition, we introduce a new method for the selection of systems to be used for data fusion. For this purpose, we use the bias concept that measures the deviation of a system from the norm or majority and employ the systems with higher bias in the data fusion process. This approach provides even higher correlations with the human-based results. We demonstrate that our approach outperforms the previously proposed automatic ranking methods.