基于滑动窗口模型的数据流闭合高效用项集挖掘

从数据流中挖掘高效用项集是一项具有挑战性的任务,因为传入的数据必须在时间和存储内存约束下进行实时处理数据流挖掘通常会产生大量冗余的项集,为了减少这些无用的项集数量且保证无损压缩,需要挖掘闭合项集,它可以比全集高效用项集的集合小几个数量级.为了解决以上问题,提出一种基于滑动窗口模型的数据流闭合高效用项集挖掘(closed high utility itemsets mining over data stream based on sliding window model,CHUI_DS)算法.在 CHUI DS 中设计了 一种新的效用列表结构,该结构在提升批次插入和删除的速度方面非常有效此外,应用修剪策略来改进闭合项集挖掘过程,消除潜在的低效用候选对象.对真实数据集和合成数据集进行的广泛实验评估显示了该算法的效率以及可行性就速度而言,它优于先前提出的主要以批处理模式运行的算法.且它适用于不同大小的滑动窗口,在事务数量等方面具有较强的扩展性.     

减少候选项集的数据流高效用项集挖掘算法

大数据环境下高效用项集挖掘算法中过多的候选项集极大地降低了算法的时空效率,提出了一种减少候选项集的数据流高效用项集挖掘算法。首先,通过数据流中当前窗口的一次扫描建立一个全局树,并降低全局树中头表入口与节点的冗余效用值;然后,基于全局树生成候选模式,基于增长算法降低局部树的候选项集效用;最终,从候选模式中选出高效用模式。基于真实数据流的实验结果表明,本算法的时空效率与内存占用比均优于其他数据流的高效用模式挖掘算法。     

基于树结构的高效关联规则挖掘算法*

关联规则挖掘过程中,大量候选项集的产生成为影响挖掘效率提高的一个主要因素。针对这一问题,提出了一种基于树结构的关联规则挖掘算法。该算法运用关联矩阵将频繁项集映射到树结构中存储,并利用树中包含部分频繁项集的子树,逐步拓展成包含所有频繁项集的树结构;其不仅提高了候选项集的生成效率,而且极大地减少了候选项集的产生数量。实验证明,该算法相比同类算法是快速有效的。     

基于效用表的快速高平均效用挖掘算法

高效用项集挖掘在数据挖掘领域中受到了广泛的关注,但是高效用项集挖掘并没有考虑项集长度对效用值的影响,所以高平均效用项集挖掘被提出;而目前的一些高平均效用项集挖掘算法需要耗费大量的时间才能挖掘出有效的高平均效用项集。针对此问题,给出了一个高平均效用项集挖掘的改进算法——FHAUI。FHAUI算法将效用信息保存到效用列表中,通过效用列表的比较来挖掘出所有的高平均效用值,同时FHAUI算法还采用了一个二维矩阵来有效减少二项效用值的连接比较次数。最后将FHAUI算法在多个经典的数据集上测试。实验结果表明,FHAUI算法在效用列表的连接比较次数上有了极大的降低,同时其时间性能也有非常大提高。     

一种高效用项集并行挖掘算法

由于能反映用户的偏好,可以弥补传统频繁项集挖掘仅由支持度来衡量项集重要性的不足,高效用项集正在成为当前数据挖掘研究的热点。为使高效用项集挖掘更好地适应数据规模不断增大的实际需求,提出了一种高效用项集的并行挖掘算法PHUI-Mine。提出了记录挖掘高效用项集信息的DHUI-树结构,描述了DHUI-树的构造方法,论证了DHUI-树的动态剪枝策略。在此基础上,给出了高效用项集挖掘的并行算法描述。实验结果表明,PHUI-Mine算法具有较高的挖掘效率及较低的存储开销。     

基于快速高效用项集挖掘的大规模消息流预测算法研究与应用

为了提高大规模消息流话题预测的准确性与效率,提出基于高效用项集挖掘的消息流话题预测算法。计算时间窗口中词汇的内部效用与外部效用,根据会话内所有词汇的效用计算最小效用值;采用高效用项集挖掘算法产生候选话题模式集,随之提取最终的话题模式。为了提高高效用项集挖掘的时间效率与存储效率,设计三角项集效用树保存项集的效用信息,设计话题搜索树保存候选话题模式集。最终基于真实消息流数据集进行实验,结果显示该算法有效地提高了话题预测的准确率,并且实现了较快的响应时间。     

精简高效用模式挖掘综述

全集高效用模式挖掘算法存在的关键问题之一是会产生冗余的高效用项集,这将导致用户很难在大量的高效用项集中发现有用的信息,严重降低了高效用模式挖掘算法的性能。为解决这一问题,衍生出了精简高效用模式挖掘算法,其主要包括最大高效用模式、闭合高效用模式、top-k高效用模式以及三者之间的组合高效用模式挖掘算法等。首先,介绍了精简高效用模式的相关问题描述;然后,从有无候选项集生成、一两阶段挖掘方法、数据结构类型和剪枝策略等角度,重点分类总结了精简高效用模式挖掘方法;最后,给出了精简高效用模式的进一步研究方向,包括处理基于负项的高效用精简模式、处理基于时间的高效用精简模式及处理动态复杂的数据等。     

基于R-list的Top-K高效用项集挖掘算法

针对现有的一阶段Top-K高效用项集挖掘算法挖掘过程中阈值提升慢,迭代时生成大量候选项集造成内存占用过多等问题,提出一种基于重用链表(R-list)的Top-K高效用挖掘算法RHUM。使用一种新的数据结构R-list来存储并快速访问项集信息,无需第2次扫描数据库进行项集挖掘。该算法重用内存以保存候选集信息,结合改进的RSD阈值提升策略对数据进行预处理,期间采用更严格的剪枝参数在递归搜索的过程中同时计算多个项集的效用来缩小搜索空间。在不同类型数据集中的实验结果表明:RHUM算法在内存效率方面均优于其他一阶段算法,且在K值变化时能保持稳定。     

基于模式增长的高效用序列模式挖掘算法

高效用序列模式挖掘是数据挖掘领域的一项重要内容, 在生物信息学、消费行为分析等方面具有重要的应用.与传统基于频繁项模式挖掘方法不同, 高效用序列模式挖掘不仅考虑项集的内外效用, 更突出项集的时间序列含义, 计算复杂度较高.尽管已经有一定数量的算法被提出应用于解决该类问题, 挖掘算法的时空效率依然成为该领域的主要研究热点问题.鉴于此, 本文提出一个基于模式增长的高效用序列模式挖掘算法HUSP-FP.依据高效用序列项集必须满足事务效用闭包属性要求, 算法首先在去除无用项后建立全局树, 进而采用模式增长方法从全局树上获取全部高效用序列模式, 避免产生候选项集. 在实验环节与目前效率较好的HUSP-Miner、USPAN、HUS-Span三类算法进行了时空计算对比, 实验结果表明本文给出算法在较小阈值下仍能有效挖掘到相关序列模式, 并且在计算时间和空间使用效率两方面取得了较大的提高.     

HHUIM：一种新的启发式高效用项集挖掘方法

针对基于启发式的高效用项集挖掘算法在挖掘过程中可能丢失大量项集的问题,提出一种新的启发式高效用项集挖掘算法HHUIM。HHUIM利用哈里斯鹰优化算法进行种群的更新,能够有效减少项集的丢失。提出并设计了鹰的替换策略,解决了搜索空间较大的问题,降低了适应度函数值低于最小效用阈值的鹰的数量。此外,提出存储回溯策略,可有效防止算法收敛过快达到局部最优。大量的实验表明,所提算法优于目前最先进的启发式高效用项集挖掘算法。     

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。     

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.     

Editorial: Special issue on recent advances in hybrid control theory and applications

正The demands of a rapidly advancing technology for faster and more accurate controllers have always had a strong influence on the progress of automatic control theory.In recent years control problems have been arising with increasing frequency in widely different areas,which cannot be addressed using conventional control techniques.The principal reason for this is the fact that a highly competitive economy is forcing systems to operate in regimes where     

Information for Authors

正Aim The Journals of Zhejiang University-SCIENCE(A/B/C)areedited by the international board of distinguished Chinese andforeign scientists,and are aimed to present the latest devel-opments and achievements in scientific research in China andoverseas to the world’s scientific circles,especially to stimulateand promote academic exchange between Chinese and for-eign scientists everywhere.     

Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance

The relative concentrations of different pigments within a leaf have significant physiological and spectral consequences. Photosynthesis, light use efficiency, mass and energy exchange, and stress response are dependent on relationships among an ensemble of pigments. This ensemble also determines the visible characteristics of a leaf, which can be measured remotely and used to quantify leaf biochemistry and structure. But current remote sensing approaches are limited in their ability to resolve individual pigments. This paper focuses on the incorporation of three pigments—chlorophyll a, chlorophyll b, and total carotenoids—into the LIBERTY leaf radiative transfer model to better understand relationships between leaf biochemical, biophysical, and spectral properties.Pinus ponderosa and Pinus jeffreyi needles were collected from three sites in the California Sierra Nevada. Hemispheric single-leaf visible reflectance and transmittance and concentrations of chlorophylls a and b and total carotenoids of fresh needles were measured. These data were input to the enhanced LIBERTY model to estimate optical and biochemical properties of pine needles. The enhanced model successfully estimated reflectance (RMSE = 0.0255, BIAS = 0.00477, RMS%E = 16.7%), had variable success estimating transmittance (RMSE = 0.0442, BIAS = 0.0294, RMS%E = 181%), and generated very good estimates of carotenoid concentrations (RMSE = 2.48 µg/cm², BIAS = 0.143 µg/cm², RMS%E = 20.4%), good estimates of chlorophyll a concentrations (RMSE = 10.7 µg/cm², BIAS = − 0.992 µg/cm², RMS%E = 21.1%), and fair estimates of chlorophyll b concentrations (RMSE = 7.49 µg/cm², BIAS = − 2.12 µg/cm², RMS%E = 43.7%). Overall root mean squared errors of reflectance, transmittance, and pigment concentration estimates were lower for the three-pigment model than for the single-pigment model. The algorithm to estimate three in vivo specific absorption coefficients is robust, although estimated values are distorted by inconsistencies in model biophysics. The capacity to invert the model from single-leaf reflectance and transmittance was added to the model so it could be coupled with vegetation canopy models to estimate canopy biochemistry from remotely sensed data.     

Mr. Twinn’s bombes

This article discusses the history and design of the special versions of the bombe key-finding machines used by Britain’s Government Code & Cypher School (GC&CS) during World War II to attack the Enigma traffic of the Abwehr (the German military intelligence service). These special bombes were based on the design of their more numerous counterparts used against the traffic of the German armed services, but differed from them in important ways that highlight the adaptability of the British bombe design, and the power and flexibility of the diagonal board. Also discussed are the changes in the Abwehr indicating system that drove the development of these machines, the ingenious ways in which they were used, and some related developments involving the bombes used by the U.S. Navy’s cryptanalytic unit (OP-20-G).