基于不均匀密度的自动聚类算法

针对基于密度的聚类算法不能自动处理密度分布不均匀的数据问题,提出一种基于不均匀密度的自动聚类算法。该算法既保持了一般基于密度算法的优点,也能有效地处理分布不均匀的数据。实验结果表明,该算法是有效的。     

基于网格的多密度增量聚类算法

提出一种基于网格的多密度增量聚类算法MICG,定义含网格单元间的相对密度和重心距离的判别函数。当数据集的部分数据发生变动后,不需要对全部数据重新聚类,只需分析有数据变更的单元与邻居单元的关系,结合原有的聚类结果形成新的聚类,有效地提高了聚类分析的效率。时间复杂度与空间复杂度同数据集大小、属性个数成线性关系。实验结果表明,MICG算法能够处理任意形状和不同密度的类,有效地解决数据更新时的增量聚类问题。     

基于网格相对密度的多密度聚类算法

提出网格相对密度的概念和边界点提取技术,在此基础上给出了一种多密度聚类算法。该算法使用网格相对密度识别具有不同密度聚簇的相对高密度网格单元,聚类时从相对高密度网格单元开始逐步扩展生成聚簇。实验结果表明,算法能有效地识别不同形状、不同密度的聚簇并对噪声数据不敏感,具有聚类精度高等优点。     

基于区域划分的DBSCAN多密度聚类算法*

DBSCAN聚类算法使用固定的Eps和Minpts,处理多密度的数据效果不理想;并且算法的时间复杂度为O(n2)。针对以上问题,提出一种基于区域划分的DBSCAN多密度聚类算法。算法利用网格相对密度差把数据空间划分成密度不同的区域,每个区域的Eps根据该区域的密度计算自动获得,并利用DBSCAN算法进行聚类,提升了DBSCAN的精度;避免了DBSCAN在查找密度相连时需要遍历所有数据,从而改善了算法效率。实验表明算法能有效地对多密度数据进行聚类,对各种数据的适应力较强,效率较优。     

基于相对密度的密度峰值聚类算法

密度峰值聚类算法在处理密度不均匀的数据集时易将低密度簇划分到高密度簇中或将高密度簇分为多个子簇,且在样本点分配过程中存在误差传递问题。提出一种基于相对密度的密度峰值聚类算法。引入自然最近邻域内的样本点信息,给出新的局部密度计算方法并计算相对密度。在绘制决策图确定聚类中心后,基于对簇间密度差异的考虑,提出密度因子计算各个簇的聚类距离,根据聚类距离对剩余样本点进行划分,实现不同形状、不同密度数据集的聚类。在合成数据集和真实数据集上进行实验,结果表明,该算法的FMI、ARI和NMI指标较经典的密度峰值聚类算法和其他3种聚类算法分别平均提高约14、26和21个百分点,并且在簇间密度相差较大的数据集上能够准确识别聚类中心和分配剩余的样本点。     

自动确定聚类中心的密度峰聚类

密度峰聚类是一种新的基于密度的聚类算法,该算法不需要预先指定聚类数目,能够发现非球形簇。针对密度峰聚类算法需要人工确定聚类中心的缺陷,提出了一种自动确定聚类中心的密度峰聚类算法。首先,计算每个数据点的局部密度和该点到具有更高密度数据点的最短距离;其次,根据排序图自动确定聚类中心;最后,将剩下的每个数据点分配到比其密度更高且距其最近的数据点所属的类别,并根据边界密度识别噪声点,得到聚类结果。将新算法与原密度峰算法进行对比,在人工数据集和UCI数据集上的实验表明,新算法不仅能够自动确定聚类中心,而且具有更高的准确率。     

复杂高维数据的密度峰值快速搜索聚类算法

机器学习的无监督聚类算法已被广泛应用于各种目标识别任务。基于密度峰值的快速搜索聚类算法(DPC)能快速有效地确定聚类中心点和类个数,但在处理复杂分布形状的数据和高维图像数据时仍存在聚类中心点不容易确定、类数偏少等问题。为了提高其处理复杂高维数据的鲁棒性,文中提出了一种基于学习特征表示的密度峰值快速搜索聚类算法(AE-MDPC)。该算法采用无监督的自动编码器(AutoEncoder)学出数据的最优特征表示,结合能刻画数据全局一致性的流形相似性,提高了同类数据间的紧致性和不同类数据间的分离性,促使潜在类中心点的密度值成为局部最大。在4个人工数据集和4个真实图像数据集上将AE-MDPC与经典的K-means,DBSCAN,DPC算法以及结合了PCA的DPC算法进行比较。实验结果表明,在外部评价指标聚类精度、内部评价指标调整互信息和调整兰德指数上,AE-MDPC的聚类性能优于对比算法,而且提供了更好的可视化性能。总之,基于特征表示学习且结合流形距离的AE-MDPC算法能有效地处理复杂流形数据和高维图像数据。     

不确定数据的最优?? 近邻和局部密度聚类算法

传统聚类算法一般针对的是确定数据, 无法解决不确定数据的聚类问题; 现有基于密度的不确定数据聚类算法存在参数敏感且计算率低的问题. 对此, 在引进新的不确定数据相异度函数、最优?? 近邻、局部密度和互包含概念的基础上, 提出解决不确定数据聚类问题的不确定数据的最优?? 近邻和局部密度聚类(OLUC) 算法. 该算法不仅能降低参数敏感性, 提高计算效率, 而且具有动态自适应优化?? 近邻, 快速发现聚类中心和除噪优化的能力. 实验结果表明, 所提出的算法对无论是否存在噪声的不确定数据集都效果良好.

     

一种分布式隐私保护的密度聚类算法

对基于密度的分布式聚类算法DBDC进行改进,提出了一种基于密度的分布式隐私保护聚类算法DBPPDC。在由局部模型确定全局模型时,通过相关安全协议有效地保护了局部模型,同时不影响全局聚类。在利用全局模型更新局部模型时,通过改进算法、应用安全协议保护隐私信息,最终使各站点分布的数据能够安全聚类。理论分析和实验结果表明,DBPPDC算法是有效的。     

聚类算法综述

数据挖掘技术可以从大量数据中发现潜在的、有价值的知识,它给人们在信息时代所积累的海量数据赋予了新的意义。随着数据挖掘技术的迅速发展,作为其重要的组成部分,网格聚类技术已经被广泛应用于数据分析、图像处理、市场研究等许多领域。网格聚类算法研究已经成为数据挖掘研究领域中非常活跃的一个研究课题。 介绍了数据挖掘理论,对网格聚类算法进行了深入的分析研究。在研究了传统网格聚类算法的基础上,提出了一些改进的网格聚类算法,这些算法相比传统网格聚类算法有更好的聚类质量和效率。在分析了传统的多密度聚类算法的基础上,提出了基于网格的多密度聚类算法(Grid-based Clustering Algorithm for Multi-density)[1],该算法主要采用密度阈值递减的多阶段聚类技术提取不同密度的聚类,同时对聚类结果进行了人工干预。研究结果表明,基于网格的多密度聚类算法不仅能够对数据集进行正确的聚类,同时还能有效地弥补孤立点检测,有效地解决了传统多密度聚类算法不能有效识别孤立点和噪声的缺陷。基于网格的多密度聚类算法比传统的共享近邻SNN算法精度高,适合于均匀密度数据集、大部分多密度数据集,并且可以发现任意形状的聚类,对噪声数据和数据输入顺序不敏感,但对小部分多密度数据集的聚类结果不理想[1]。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.