Using Greedy algorithm: DBSCAN revisited II

INTRODUCTION Clustering groups dataset data into meaning-ful subclasses in such a way that minimizes theintra-differences and maximizes the in-ter-differences of these subclasses; and is one ofthe most widely studied problems in data mining.There are many application areas for clusteringtechniques, such as statistical data analysis, patternrecognition, image processing, and other businessprocesses etc. Many clustering algorithms havebeen proposed, in part…     

Using Greedy algorithm:DBSCAN revisited Ⅱ

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise(DBSCAN)(Ester et al.,1996),and has the following advantages: first,Greedy algorithm substitutes for R*-tree(Bechmann et al.,1990)in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second,the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally,authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.     

基于改进的贪婪算法在0/1背包问题中的研究与应用

对贪婪算法的概念、特性、及其解决问题的步骤进行了阐述,结合0/1背包问题重点对贪婪算法进行了分析,总结归纳传统贪婪算法的解决方案,提出改进的贪婪算法解决策略。     

基于改进的贪婪算法在0/1背包问题中的研究与应用

对贪婪算法的概念、特性、及其解决问题的步骤进行了阐述,结合0/1背包问题重点对贪婪算法进行了分析,总结归纳传统贪婪算法的解决方案,提出改进的贪婪算法解决策略。     

求解组合拍卖问题的一种贪婪算法

为有效解决组合拍卖问题,从下模集函数最大值问题的基本结论出发,将部分穷举法与贪婪算法相结合,给出了一种求解组合拍卖问题的新算法——改进的贪婪算法,并从理论上证明了所给算法具有更好的性能保证.     

"八宝麒麟"辩证

在中国传统宫庙的诸多装饰图腾中,几乎所有官宦世家及宗祠、寺庙建筑秀面“裙堵”上,皆以所谓“八宝麒麟”装饰。也有部份身堵又以“麒麟、凤凰、牡丹”组合寓意“富贵吉祥”的砖、石雕。本研究考证发现该图腾虽然与麒麟相似,但却另有不同的名称及寓意,故以此特殊神兽雕刻作为本研究主轴,且经多方考证证实应为明太祖朱元璋所创之“（犭贪）”兽。     

Greedy algorithm in m-term approximation for periodic Besov class with mixed smoothness

Nonlinear m-term approximation plays an important role in machine learning,signal processing and statistical estimating.In this paper by means of a nondecreasing dominated function,a greedy adaptive compression numerical algorithm in the best m-term approximation with regard to tensor product wavelet.-type basis is proposed.The algorithm provides the asymptotically optimal approximation for the class of periodic functions with mixed Besov smoothness in the Lq norm.Moreover,it depends only on the expansion of function f by tensor product wavelet-type basis,but neither on q nor on any special features of f.     

0/1背包问题的贪心算法

0／1背包问题属于动态规划问题，部分背包问题属于贪心算法的范畴，通过比较两种算法的联系和区别，来寻求0／1背包问题的贪心算法的条件，用贪心算法来解决部分0／1背包问题的求解。     

改进的基于遗传算法与FCSS相结合的模糊球壳聚类算法

遗传算法（GA）被广泛地应用在聚类算法中.但是当数据点多时,其计算量大的问题是不容忽视的.针对与遗传算法相结合的FCSS算法中的这一不足,主要通过研究了GA初始化种群的选取方法,对GA-FCSS算法进行了改进.实验数据表明：改进的GA-FCSS算法在收敛速度方面有令人满意的效果.     

A statistical information-based clustering approach in distance space

Clustering, as a powerful data mining technique for discovering interesting data distributions and patterns in the underlying database, is used in many fields, such as statistical data analysis, pattern recognition, image processing, and other business applications. Density-based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996) is a good performance clustering method for dealing with spatial data although it leaves many problems to be solved. For example,DBSCAN requires a necessary user-specified threshold while its computation is extremely time-consuming by current method such as OPTICS, etc. (Ankerst et al., 1999), and the performance of DBSCAN under different norms has yet to be examined. In this paper, we first developed a method based on statistical information of distance space in database to determine the necessary threshold. Then our examination of the DBSCAN performance under different norms showed that there was determinable relation between them. Finally, we used two artificial databases to verify the effectiveness and efficiency of the proposed methods.     

SSAS聚类分析算法在顾客分组分析中的应用

介绍一种利用数据挖掘技术建立顾客分类分析的挖掘模型。主要介绍了基于Microsoft SQL SERVER Analysis Services(SSAS)的聚类分析算法,以及SSAS聚类分析算法在对某销售信息进行顾客分组分析中的应用,通过分析聚类分析算法挖掘模型所发现的模式,得出了对顾客分类的结果,为销售公司管理层的营销决策提供有力的参考和辅助。     

A statistical information-based clustering approach in distance space

Clustering, as a powerful data mining technique for discovering interesting data distributions and patterns in the nderlying database, is used in many fields, such as statistical data analysis, pattern recognition, image processing, and other usiness applications. Density-based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996) is a good erformance clustering method for dealing with spatial data although it leaves many problems to be solved. For example, BSCA…     

贪心算法与动态规划的比较

介绍了计算机算法设计的两种常用算法思想:贪心算法与动态规划算法。通过介绍两种算法思想的基本原理,比较两种算法的联系和区别。通过背包问题对比了两种算法的使用特点和使用范围。     

CTW无损压缩算法在管道无损检测中的应用

超声和漏磁无损检测方法是目前输油管道常用的安全检测方法,然而其检测数据庞大,必须对数据进行压缩。介绍了一种基于CTW(context tree weight)的无损压缩算法,该算法采用了新的更低冗余度的概率估算法,具有速度快和抗差错能力强等特点,将该算法应用于输油管道超声和漏磁方法无损检测实验数据的无损压缩,得到了较高的压缩率,与LZW(lempel ziv welch)无损压缩算法相比获得了更高的压缩率。     

顶点覆盖问题的贪心算法在网格简化中的应用研究

针对三角网格简化,设计了求解顶点覆盖问题的贪心算法,通过贪心选择最小的顶点集去＂覆盖＂边集,同时保留被简化网格的特征信息,自动实现最大程度简化。给出的实例也表明简化后的网格质量良好,算法既降低了时间复杂度又保持了原形状的特征信息。     

基于密度的聚类算法研究

聚类算法是数据挖掘的核心技术,基于密度的聚类是一类已经被证明非常有效的聚类方法．与DBSCAN算法作比较,文章提出了一种基于密度的聚类算法（Clustering Using Centers and Density,CUCD）．该算法是基于中心点以及密度实现的,其核心对象是根据数据分布计算出来的虚拟的点,并且核心对象的代表性随程序的执行次数而提高;经实验验证,该算法具有较好的时间效率和聚类质量．     

求解TSP问题的几种算法比较

旅行售货商问题(简称TSP)是离散优化的一个经典的重要问题,对求解算法的研究非常重要。在介绍求解TSP问题的贪婪算法、禁忌搜索算法、模拟退火算法、遗传算法的基本思想之后,提出了相应的算法。针对测试库的四个典型算例,用程序实现这些算法,对这些算法的运行时间和结果进行比较研究。结果表明贪婪算法短时间就可以得出解,禁忌搜索算法与遗传算法的效果相当,模拟退火算法比遗传算法的结果好。     

排课系统开发中贪婪和回溯算法的应用

排课系统作为数字化校园建设中的一项重要组成部分,现在越来越引起人们的重视。一个好的排课系统可以极大的简化目前繁重的高校排课工作,而作为一个好的排课系统在开发设计过程中算法的采用更是关键。     

基于局部泛化误差模型的RBFNN的启发式训练方法

研究如何应用吴永贤（W.W.Y.NG）提出的局部泛化误差模型来训练径向基函数神经网络（RBFNN）,给出了一种训练RBFNN的启发式训练方法.实验表明,该方法成功解决了模型结果计算时间复杂度问题,同时RBFNN的训练精度也达到令人满意的结果.     

Application-adaptive resource scheduling in a computational grid

INTRODUCTION The computational grid (Foster and Kesselman, 1998) combines computing, network and storage resources to support the running of large-scale ap- plications, which may be computation-intensive or communication-intensive. There are many technical challenges for deploying large-scale applications over this distributed computing environment due to the vast diversity of resources involved. Efficient and application-adaptive resource management and scheduling are challenging tasks i…