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聚类研究，实现了基于距离，基于密度和改进算法-clustering, based on the distance to achieve, based on density and improved algorithm
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Major clustering methods
Partitioning methods
Hierarchical methods
Density-based methods
Grid-based methods
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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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clustering different algorithms, K means, density based, hierarchical
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Mining trajectory data has been gaining significant interest in recent years. However, existing approaches to trajectory
clustering are mainly based on density and Euclidean distance measures. We argue that when the utility of spatial clustering of
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社会网络分析中的密度估计方法，发表在sigmod2014上的深度文章，有具体算法和实验评价-Social network analysis density estimation method was published in sigmod2014 good text, be able to estimate the social networks of small groups and core.
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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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science上密度峰值聚类算法源码，包括matlab源码和s1数据集(Source code for peak density clustering algorithm on Science)
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