多指标面板数据的聚类分析及其应用

多指标面板数据的多元统计分析在国内研究中尚属空白.本文分析了面板数据的数据格式和数字特征,根据聚类分析原理,重新构造了多指标面板数据的距离函数和离差平方和函数,在此基础上,说明了多指标面板数据的聚类分析过程.最后对我国各地区工业企业生产效率进行了聚类实证分析,显示了良好的效果。     

可变模糊集合理论与可变模型集

在对立模糊集定义基础上给出以相对隶属函数表示的模糊可变集合定义,给出可变模糊聚类迭代模型、可变模糊模式识别模型、可变模糊对立识别模型.它们是可变模糊聚类、识别、优选决策、评价相统一的理论模型集,是可变模糊集的基础模型与核心内容,可用于自然、管理、人文、社会等各种学科中关于模糊聚类、识别、优选决策、评价、预测等众多实际领域.     

A rule-based development of incremental models

In the study, we propose a concept of incremental fuzzy models in which fuzzy rules are aimed at compensating discrepancies resulting because of the use of a certain global yet simple model of general nature (such as e.g., a constant or linear regression). The structure of input data and error discovered through fuzzy clustering is captured in the form of a collection of fuzzy clusters, which helps eliminate (compensate) error produced by the global model. We discuss a detailed architecture of the proposed rule-based model and present its design based on an augmented version of Fuzzy C-Means (FCM). An extended suite of experimental studies offering some comparative analysis is covered as well.     

基于最大最小蚂蚁系统的物流配送中心选址算法的研究

提出了一种基于信息素自适应调节的最大最小蚂蚁系统的多物流配送中心选址算法,利用改进的蚁群算法的路径寻优机制结合蚂蚁聚集尸体的行为模式,根据物流配送总成本最低的原则将各配送点与候选配送中心进行聚类,合理选择配送中心。将已有物流配送模型进行拓展,加入经营管理成本。分别利用基本蚁群聚类算法和改进的蚁群聚类算法对配送中心选址进行仿真,实验结果表明在解决大规模配送中心选址问题时,改进的算法在解的质量和收敛速度方面明显优于基本蚁群聚类算法。     

股指期货套利中的最优现货组合构建策略研究

针对如何构建与股指期货联动性较好的现货组合问题,本文提出采用两阶段优化策略以提高组合的跟踪准确度。第一阶段,利用基于独立成分分析与模糊C均值算法相结合的时间序列聚类方法将沪深300股指期货对应的成分股进行聚类;第二阶段,对聚类之后的结果进行指数优化复制,以跟踪误差最小为目标,确定跟踪组合的成分股权重。实证研究表明,本文所提出的两阶段优化策略可以较好地改进指数跟踪效果。     

基于无线通信基站的室内三维定位问题

三维定位问题是现代商用通信网络中对于定位系统存在的一个真正具有技术难度的挑战.根据视距传播环境和非视距传播环境的到达时间的数据集,建立线性误差模型;对于无真实位置的竞赛数据集,定义竞赛数据定位误差评估模型;基于不同的空间场景,提出基于空间单元的定位算法;面对高度误差明显高于平面误差的问题,设计基于高斯加权的误差补偿模型;针对最优定位精度最少基站问题,提出基于贪心策略的基站选择算法;考虑轨迹连续性,设计轨迹准确性验证的10-fold交叉验证方法;基于测量距离有限的真实环境,分析平均"连接度数"与定位精度的关系.实验结果表明,提出的定位算法在有效基站数大于等于5时,能获得较好的定位精度.     

基于矩阵变换的聚类集成优化模型

聚类集成方法能够有效综合不同的聚类结果,提高聚类的精确度和稳定性.提出了一个基于矩阵变换的聚类集成优化模型,模型通过矩阵变换代替传统方法中的聚类配准模式,使得优化模型更加简洁,然后给出了求解该优化模型的叠代算法.实验表明,提出的聚类集成方法能够有效提高聚类集成的稳定性和精确度,并且在聚类数目比较少时,算法有着较低的时间复杂度.     

A new clustering approach using data envelopment analysis

In this paper, we present a new clustering method that involves data envelopment analysis (DEA). The proposed DEA-based clustering approach employs the piecewise production functions derived from the DEA method to cluster the data with input and output items. Thus, each evaluated decision-making unit (DMU) not only knows the cluster that it belongs to, but also checks the production function type that it confronts. It is important for managerial decision-making where decision-makers are interested in knowing the changes required in combining input resources so it can be classified into a desired cluster/class. In particular, we examine the fundamental CCR model to set up the DEA clustering approach. While this approach has been carried for the CCR model, the proposed approach can be easily extended to other DEA models without loss of generality. Two examples are given to explain the use and effectiveness of the proposed DEA-based clustering method.     

单体型装配问题及其算法

单核苷酸多态性(SNP)单体型装配问题就是从给定的来自某人染色体的SNP片段中去除错误,重构出尽可能与原来片段一致的单体型.这个问题有几个不同的模型最少片段去除(MFR)问题,最少SNP去除(MSR)问题以及最少错误纠正(MEC)问题.前两个问题的复杂性与算法已有一些学者研究过.第三个问题已被证明是NP完全问题,但这个问题的实际算法还没有.该文对MEC问题给出了一个分支定界算法,这个算法能得到问题的全局最优解.通过这个算法对实际数据的计算说明了MEC模型的合理性,即在一定条件下,通过修正最少的错误重构出的单体型确实是真实的单体型.由于分支定界算法对这样一个NP完全问题不能在可接受的时间内解规模较大的问题,文中又给出了求解MEC问题的两个基于动态聚类的算法,以便对规模较大的问题在可接受的时间内得到近似最优解.数值实际表明这两个算法很快,很有效.这两个算法总能得到与分支定界找到的全局最优解很接近的近似最优解.鉴于MEC问题是NP完全的,这两个算法是有效的、实际的算法.     

A Network Analysis Model for Disambiguation of Names in Lists

In research and application, social networks are increasingly extracted from relationships inferred by name collocations in text-based documents. Despite the fact that names represent real entities, names are not unique identifiers and it is often unclear when two name observations correspond to the same underlying entity. One confounder stems from ambiguity, in which the same name correctly references multiple entities. Prior name disambiguation methods measured similarity between two names as a function of their respective documents. In this paper, we propose an alternative similarity metric based on the probability of walking from one ambiguous name to another in a random walk of the social network constructed from all documents. We experimentally validate our model on actor-actor relationships derived from the Internet Movie Database. Using a global similarity threshold, we demonstrate random walks achieve a significant increase in disambiguation capability in comparison to prior models. Bradley A. Malin is a Ph.D. candidate in the School of Computer Science at Carnegie Mellon University. He is an NSF IGERT fellow in the Center for Computational Analysis of Social and Organizational Systems (CASOS) and a researcher at the Laboratory for International Data Privacy. His research is interdisciplinary and combines aspects of bioinformatics, data forensics, data privacy and security, entity resolution, and public policy. He has developed learning algorithms for surveillance in distributed systems and designed formal models for the evaluation and the improvement of privacy enhancing technologies in real world environments, including healthcare and the Internet. His research on privacy in genomic databases has received several awards from the American Medical Informatics Association and has been cited in congressional briefings on health data privacy. He currently serves as managing editor of the Journal of Privacy Technology. Edoardo M. Airoldi is a Ph.D. student in the School of Computer Science at Carnegie Mellon University. Currently, he is a researcher in the CASOS group and at the Center for Automated Learning and Discovery. His methodology is based on probability theory, approximation theorems, discrete mathematics and their geometries. His research interests include data mining and machine learning techniques for temporal and relational data, data linkage and data privacy, with important applications to dynamic networks, biological sequences and large collections of texts. His research on dynamic network tomography is the state-of-the-art for recovering information about who is communicating to whom in a network, and was awarded honors from the ACM SIG-KDD community. Several companies focusing on information extraction have adopted his methodology for text analysis. He is currently investigating practical and theoretical aspects of hierarchical mixture models for temporal and relational data, and an abstract theory of data linkage. Kathleen M. Carley is a Professor of Computer Science in ISRI, School of Computer Science at Carnegie Mellon University. She received her Ph.D. from Harvard in Sociology. Her research combines cognitive science, social and dynamic networks, and computer science (particularly artificial intelligence and machine learning techniques) to address complex social and organizational problems. Her specific research areas are computational social and organization science, social adaptation and evolution, social and dynamic network analysis, and computational text analysis. Her models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale tools she and the CASOS group have developed are: BioWar a city, scale model of weaponized biological attacks and response; Construct a models of the co-evolution of social and knowledge networks; and ORA a statistical toolkit for dynamic social Network data.