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层次AGNES聚类挖掘,本程序用MFC强力打造经典算法,程序附有要处理的数据集,程序源代码有很详细的注释,功能基本实现,但是也有部分BUG,望广大用户斧正 。-AGNES clustering Hierarchical mining, the program uses MFC to create a strong classical algorithm, the program to deal with data sets, the program source code with very d
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Web文档聚类系统的设计与实现:数据挖掘;聚类分柝:文本挖掘;预处理;聚类组合;可
视化;欧氏距离-Web Document Clustering Design and Implementation: Data mining Clustering Hierarchical: text mining pretreatment cluster combinations visualization Euclidean distance
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hierarchical data clustering
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hierarchical data clustering
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The cluster head is selected by the random number
generated by nodes in the hierarchical routing protocol which
is implementing Low Energy Adaptive Clustering Hierarchy
(LEACH). As a result, the cluster head may be chosen
randomly and be
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clustering different algorithms, K means, density based, hierarchical
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WSNs的LEACH路由算法,它是一种层次型算法,核心思想是分簇,并轮流当选簇头!-WSNs LEACH routing algorithm, which is a hierarchical algorithm, the core idea is clustering, and turns elected cluster head!
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Clustering based leach protocol using C++ in Castalia simulator. It is the first hierarchical protocol implemented in WSN
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Low Energy Adaptive Clustering Hierarchical Protocol for Wireless sensor networks
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本文的主要贡献是以线元为基本造型单位,运用动力学原理模拟瀑布的运动轨迹[9],结合层次包围盒技术和建立聚类树加速粒子与障碍物之间的碰撞检测,并采用逐渐淡化融合的方法绘制粒子,形成了具有真实感的实时瀑布飞溅的效果。-Contribution of this article is based on the line for basic modeling unit, use the dynamics simulation trajectories of waterfall [9], combined
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Wireless Sensor Network is the network of power-limited sensing devices called sensors. Wireless sensor network is differ other networks in terms of optimization of amount of energy because when these sensors sense and transmit data to other sensors
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Hierarchical Energy Efficient Clustering Algorithm for WSN
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ns-2_DHAC algorithm simulation source code
Using hierarchical agglomerative clustering in wireless sensor networks:
An energy-efficient and flexible approach
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本论文在对各种算法深入分析的基础上,尤其在对基于密度的聚类算法、基于层次的聚类算法和基于划分的聚类算法的深入研究的基础上,提出了一种新的基于密度和层次的快速聚类算法。该算法保持了基于密度聚类算法发现任意形状簇的优点,而且具有近似线性的时间复杂性,因此该算法适合对大规模数据的挖掘。理论分析和实验结果也证明了基于密度和层次的聚类算法具有处理任意形状簇的聚类、对噪音数据不敏感的特点,并且其执行效率明显高于传统的DBSCAN算法。-Based on the analysis on clustering
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Contour Detection and Hierarchical Image Segmentation (UC Berkeley)
MATLAB/C++混编
Arbela?ez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour Detection and Hierarchical Image Segmentation. IEEE Transactions on Pattern Analysis and Machine
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复杂网络构建模型,可能有小世界模型和无标度网络模型(Most social, biological, and technological networks display substantial non-trivial topological features, with patterns of connection between their elements that are neither purely regular nor purely random. Such features in
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实现的cure聚类的demo。算法在开始时,每个点都是一个簇,然后将距离最近的簇结合,一直到簇的个数为要求的K。它是一种分裂的层次聚类。算法分为以下6步:
1)从源数据对象中抽取一个随机样本S。
2)将样本S分割为一组划分。
3)对划分局部的聚类。
4)通过随机取样提出孤立点。如果一个簇增长得太慢,就去掉它。
5)对局部的簇进行聚类。
6)用相应的簇标签标记数据。(The implementation of the cure clustering of the
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