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基于密度的有实体障碍约束的聚类算法例程。-Have a physical density-based clustering algorithm routine obstacle constraints.
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数据挖掘中聚类算发中基于密度的算法DBSCAN-Hair count data mining clustering algorithm DBSCAN density-based
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本文提出了一种节点密度和剩余能量传感器网络加权分群算法，该分群算法优化了群首
选择，均衡了网络节点的能量消耗，延长了网络的生命周期-This paper proposes a residual energy of sensor nodes in the network density and weighted clustering algorithm, the clustering algorithm to optimize the group' s first choice, bal
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OPTICS ("Ordering Points To Identify the Clustering Structure") is an algorithm for finding density-based clusters in spatial data. It was
presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jö rg Sander[1]. Its basic idea is
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DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm
proposed by Martin Ester, Hans-Peter Kriegel, Jö rg Sander and Xiaowei Xu in 1996.[1] It is a density-based
clustering algorithm because it fi
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ector quantization is a classical quantization technique from signal processing which allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a l
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实现基于密度的聚类算法,可以处理噪声数据，效果好-density-based clustering
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聚类算法实现，基于密度的聚类算法，该算法能够用于对数据进行基于密度的分类-Clustering algorithm, density-based clustering algorithm, which can be used for data classification based on density
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基于密度的数据挖掘算法，可以有效的去处噪声的干扰。-Clustering the data with Density-Based Scan Algorithm with Noise (DBSCAN)
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c#实现DBSCAN算法，属于基于密度的聚类算法-C# realize DBSCAN algorithm, belong to the clustering algorithm based on density
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一种密度聚类模糊神经网络的建模方法A density based clustering fuzzy neural network modeling method-A density based clustering fuzzy neural network modeling method
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二维多密度网格聚类算法，主要是完成二维数据的网格聚类，采用的是多密度临近的算法。-Two-dimensional density grid clustering algorithm
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机器学习matlab源代码，包括多分类SVM，模式识别，特征选择，回归等算法。-The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be compared with
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DBSCAN算法是一种基于密度的算法，进行DBSCAN聚类-simple DBSCAN clustering algorithm，which is based on density.
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clustering different algorithms, K means, density based, hierarchical
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density based algorithm for spatial clustering
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均值漂移算法应用于一维随机数据的聚类，漂移到其密度极大值-Mean shift clustering algorithm applied to the one-dimensional random data, drift to the density maxima
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论文 在使用无线传感器网络进行目标检测时,如何布置尽可能少的传感器节点而同时实现高的正确检测概率和
低的误警率,是关键问题之一。采用数据融合技术,能实现传感器节点之间的协同,从而大幅提高目标检测精度。提
出了用于目标检测的精度模型,分析了数据融合半径与传感器节点密度之间的关系,设计聚类方法将目标点组织成布
置单元,从高密度单元到低密度单元布置传感器节点覆盖目标区域。仿真结果表明,算法在保证检测精度的同时能有
效减少所使用的传感器节点数目。
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Sensor placeme
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java语言编写的 DBSCAN基于密度的聚类算法，可实现对数据点基于密度的聚类-java language DBSCAN density-based clustering algorithm can be realized on density-based clustering of data points
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K最邻近密度估计分类,K最邻近密度估计技术是一种分类方法，不是聚类方法。-K nearest neighbor classification density estimation, K nearest neighbor density estimation technique is a classification method, not the clustering method.
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