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ADCN: An anisotropic density‐based clustering algorithm for discovering spatial point patterns with noise

机译：ADCN：一种基于各向异性密度的聚类算法，用于发现具有噪声的空间点模式

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Abstract > Density‐based clustering algorithms such as DBSCAN have been widely used for spatial knowledge discovery as they offer several key advantages compared with other clustering algorithms. They can discover clusters with arbitrary shapes, are robust to noise, and do not require prior knowledge (or estimation) of the number of clusters. The idea of using a scan circle centered at each point with a search radius Eps to find at least MinPts points as a criterion for deriving local density is easily understandable and sufficient for exploring isotropic spatial point patterns. However, there are many cases that cannot be adequately captured this way, particularly if they involve linear features or shapes with a continuously changing density, such as a spiral. In such cases, DBSCAN tends to either create an increasing number of small clusters or add noise points into large clusters. Therefore, in this article, we propose a novel anisotropic density‐based clustering algorithm (ADCN). To motivate our work, we introduce synthetic and real‐world cases that cannot be handled sufficiently by DBSCAN (or OPTICS). We then present our clustering algorithm and test it with a wide range of cases. We demonstrate that our algorithm can perform equally as well as DBSCAN in cases that do not benefit explicitly from an anisotropic perspective, and that it outperforms DBSCAN in cases that do. Finally, we show that our approach has the same time complexity as DBSCAN and OPTICS, namely O( n log n ) when using a spatial index and O( n 2 ) otherwise. We provide an implementation and test the runtime over multiple cases. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“main”XML：Lang =“en”> <标题类型=“main”>抽象</ title> >基于密度的聚类算法，如dbscan已被广泛用于空间知识发现，因为它们提供与其他聚类算法相比的几个关键优势。它们可以发现具有任意形状的簇，对噪声具有鲁棒性，并且不需要群集数量的现有知识（或估计）。使用以搜索半径<i EPS <i EPS </ i>以每个点为中心的扫描圆的想法以找到至少 Minpts </ i>点作为导出局部密度的标准很容易理解，足以探索各向同性的空间点模式。然而，存在许多情况下不能以这种方式充分捕获，特别是如果它们涉及具有连续变化的密度的线性特征或形状，例如螺旋形。在这种情况下，DBSCAN倾向于创建越来越多的小簇，或者将噪声点添加到大集群中。因此，在本文中，我们提出了一种新的基于各向异性密度的聚类算法（ADCN）。为了激励我们的工作，我们介绍了DBSCAN（或光学）无法充分处理的合成和现实案例。然后我们介绍我们的聚类算法，并通过各种情况测试。我们证明我们的算法可以在不从各向异性的角度明确起义的情况下同样地表现和DBSCAN，并且它在那种情况下表现出DBSCAN。最后，我们表明我们的方法具有与DBSCAN和光学器件相同的时间复杂性，即O（ n </ i>日志 n </ i>）使用空间索引和o（ n否则，</ i> 2 </ sup>）。我们提供了多种情况下的运行时间。 </ p> </ abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-31674/'>《Transactions in GIS: TG》</a> <b style="margin: 0 2px;">|</b><span>2018年第1期</span><b style="margin: 0 2px;">|</b><span>共22页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Mai Gengchen&option=202" target="_blank" rel="nofollow">Mai Gengchen;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Janowicz Krzysztof&option=202" target="_blank" rel="nofollow">Janowicz Krzysztof;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Hu Yingjie&option=202" target="_blank" rel="nofollow">Hu Yingjie;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Gao Song&option=202" target="_blank" rel="nofollow">Gao Song;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Department of GeographyUniversity of CaliforniaSanta Barbara California USA;</p> <p>Department of GeographyUniversity of CaliforniaSanta Barbara California USA;</p> <p>Department of GeographyUniversity of TennesseeKnoxville Tennessee USA;</p> <p>Department of GeographyUniversity of CaliforniaSanta Barbara California USA;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/4940.html" title="测绘数据库与信息系统">测绘数据库与信息系统;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> </p> </li> </ul> </div> </div> <div class="literature cardcommon" id="literaturereference" style="display:none"> <div class="similarity "> <h3 class="all_title" id="enpatent111">引文网络</h3> <div class="referencetab clearfix"> <ul id="referencedaohang"> <li dataid="referenceul">参考文献</li> <li dataid="citationul">引证文献</li> <li dataid="commonreferenceul">共引文献</li> <li dataid="commoncitationul">同被引文献</li> <li dataid="tworeferenceul">二级参考文献</li> <li dataid="twocitationul">二级引证文献</li> </ul> </div> <div class="reference_details" id="referenceList"> <ul id="referenceul"></ul> <ul id="citationul"></ul> <ul id="commonreferenceul"></ul> <ul id="commoncitationul"></ul> 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