\section{D\+B\+S\+C\+AN$<$ Range\+Search\+Type, Point\+Selection\+Policy $>$ Class Template Reference} \label{classmlpack_1_1dbscan_1_1DBSCAN}\index{D\+B\+S\+C\+A\+N$<$ Range\+Search\+Type, Point\+Selection\+Policy $>$@{D\+B\+S\+C\+A\+N$<$ Range\+Search\+Type, Point\+Selection\+Policy $>$}} \doxyref{D\+B\+S\+C\+AN}{p.}{classmlpack_1_1dbscan_1_1DBSCAN} (Density-\/\+Based Spatial Clustering of Applications with Noise) is a clustering technique described in the following paper\+: \subsection*{Public Member Functions} \begin{DoxyCompactItemize} \item \textbf{ D\+B\+S\+C\+AN} (const double epsilon, const size\+\_\+t min\+Points, const bool batch\+Mode=true, Range\+Search\+Type range\+Search=Range\+Search\+Type(), Point\+Selection\+Policy point\+Selector=Point\+Selection\+Policy()) \begin{DoxyCompactList}\small\item\em Construct the \doxyref{D\+B\+S\+C\+AN}{p.}{classmlpack_1_1dbscan_1_1DBSCAN} object with the given parameters. \end{DoxyCompactList}\item {\footnotesize template$<$typename Mat\+Type $>$ }\\size\+\_\+t \textbf{ Cluster} (const Mat\+Type \&data, arma\+::mat \¢roids) \begin{DoxyCompactList}\small\item\em Performs \doxyref{D\+B\+S\+C\+AN}{p.}{classmlpack_1_1dbscan_1_1DBSCAN} clustering on the data, returning number of clusters and also the centroid of each cluster. \end{DoxyCompactList}\item {\footnotesize template$<$typename Mat\+Type $>$ }\\size\+\_\+t \textbf{ Cluster} (const Mat\+Type \&data, arma\+::\+Row$<$ size\+\_\+t $>$ \&assignments) \begin{DoxyCompactList}\small\item\em Performs \doxyref{D\+B\+S\+C\+AN}{p.}{classmlpack_1_1dbscan_1_1DBSCAN} clustering on the data, returning number of clusters and also the list of cluster assignments. \end{DoxyCompactList}\item {\footnotesize template$<$typename Mat\+Type $>$ }\\size\+\_\+t \textbf{ Cluster} (const Mat\+Type \&data, arma\+::\+Row$<$ size\+\_\+t $>$ \&assignments, arma\+::mat \¢roids) \begin{DoxyCompactList}\small\item\em Performs \doxyref{D\+B\+S\+C\+AN}{p.}{classmlpack_1_1dbscan_1_1DBSCAN} clustering on the data, returning number of clusters, the centroid of each cluster and also the list of cluster assignments. \end{DoxyCompactList}\end{DoxyCompactItemize} \subsection{Detailed Description} \subsubsection*{template$<$typename Range\+Search\+Type = range\+::\+Range\+Search$<$$>$, typename Point\+Selection\+Policy = Ordered\+Point\+Selection$>$\newline class mlpack\+::dbscan\+::\+D\+B\+S\+C\+A\+N$<$ Range\+Search\+Type, Point\+Selection\+Policy $>$} \doxyref{D\+B\+S\+C\+AN}{p.}{classmlpack_1_1dbscan_1_1DBSCAN} (Density-\/\+Based Spatial Clustering of Applications with Noise) is a clustering technique described in the following paper\+: \begin{DoxyCode} @inproceedings\{ester1996density, title=\{A density-based algorithm \textcolor{keywordflow}{for} discovering clusters in large spatial databases with noise.\}, author=\{Ester, M. and Kriegel, H.-P. and Sander, J. and Xu, X.\}, booktitle=\{Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD \textcolor{stringliteral}{'96)\},} \textcolor{stringliteral}{ pages=\{226--231\},} \textcolor{stringliteral}{ year=\{1996\}} \textcolor{stringliteral}{\}} \end{DoxyCode} The \doxyref{D\+B\+S\+C\+AN}{p.}{classmlpack_1_1dbscan_1_1DBSCAN} algorithm iteratively clusters points using range searches with a specified radius parameter. This implementation allows configuration of the range search technique used and the point selection strategy by means of template parameters. \begin{DoxyTemplParams}{Template Parameters} {\em Range\+Search\+Type} & Class to use for range searching. \\ \hline {\em Point\+Selection\+Policy} & Strategy for selecting next point to cluster with. \\ \hline \end{DoxyTemplParams} Definition at line 53 of file dbscan.\+hpp. \subsection{Constructor \& Destructor Documentation} \mbox{\label{classmlpack_1_1dbscan_1_1DBSCAN_add1ee82d37001657fc8978359145543b}} \index{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN@{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN}!D\+B\+S\+C\+AN@{D\+B\+S\+C\+AN}} \index{D\+B\+S\+C\+AN@{D\+B\+S\+C\+AN}!mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN@{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN}} \subsubsection{D\+B\+S\+C\+A\+N()} {\footnotesize\ttfamily \textbf{ D\+B\+S\+C\+AN} (\begin{DoxyParamCaption}\item[{const double}]{epsilon, }\item[{const size\+\_\+t}]{min\+Points, }\item[{const bool}]{batch\+Mode = {\ttfamily true}, }\item[{Range\+Search\+Type}]{range\+Search = {\ttfamily RangeSearchType()}, }\item[{Point\+Selection\+Policy}]{point\+Selector = {\ttfamily PointSelectionPolicy()} }\end{DoxyParamCaption})} Construct the \doxyref{D\+B\+S\+C\+AN}{p.}{classmlpack_1_1dbscan_1_1DBSCAN} object with the given parameters. The batch\+Mode parameter should be set to false in the case where R\+AM issues will be encountered (i.\+e. if the dataset is very large or if epsilon is large). When batch\+Mode is false, each point will be searched iteratively, which could be slower but will use less memory. \begin{DoxyParams}{Parameters} {\em epsilon} & Size of range query. \\ \hline {\em min\+Points} & Minimum number of points for each cluster. \\ \hline {\em batch\+Mode} & If true, all points are searched in batch. \\ \hline {\em range\+Search} & Optional instantiated Range\+Search object. \\ \hline {\em point\+Selector} & OptionL instantiated Point\+Selection\+Policy object. \\ \hline \end{DoxyParams} \subsection{Member Function Documentation} \mbox{\label{classmlpack_1_1dbscan_1_1DBSCAN_a224c059e24d3dcdab266b9743ec37404}} \index{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN@{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN}!Cluster@{Cluster}} \index{Cluster@{Cluster}!mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN@{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN}} \subsubsection{Cluster()\hspace{0.1cm}{\footnotesize\ttfamily [1/3]}} {\footnotesize\ttfamily size\+\_\+t Cluster (\begin{DoxyParamCaption}\item[{const Mat\+Type \&}]{data, }\item[{arma\+::mat \&}]{centroids }\end{DoxyParamCaption})} Performs \doxyref{D\+B\+S\+C\+AN}{p.}{classmlpack_1_1dbscan_1_1DBSCAN} clustering on the data, returning number of clusters and also the centroid of each cluster. \begin{DoxyTemplParams}{Template Parameters} {\em Mat\+Type} & Type of matrix (arma\+::mat or arma\+::sp\+\_\+mat). \\ \hline \end{DoxyTemplParams} \begin{DoxyParams}{Parameters} {\em data} & Dataset to cluster. \\ \hline {\em centroids} & Matrix in which centroids are stored. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1dbscan_1_1DBSCAN_a0245b1f39300ec8133bc6cc9309523c6}} \index{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN@{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN}!Cluster@{Cluster}} \index{Cluster@{Cluster}!mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN@{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN}} \subsubsection{Cluster()\hspace{0.1cm}{\footnotesize\ttfamily [2/3]}} {\footnotesize\ttfamily size\+\_\+t Cluster (\begin{DoxyParamCaption}\item[{const Mat\+Type \&}]{data, }\item[{arma\+::\+Row$<$ size\+\_\+t $>$ \&}]{assignments }\end{DoxyParamCaption})} Performs \doxyref{D\+B\+S\+C\+AN}{p.}{classmlpack_1_1dbscan_1_1DBSCAN} clustering on the data, returning number of clusters and also the list of cluster assignments. If assignments[i] == S\+I\+Z\+E\+\_\+\+M\+AX, then the point is considered \char`\"{}noise\char`\"{}. \begin{DoxyTemplParams}{Template Parameters} {\em Mat\+Type} & Type of matrix (arma\+::mat or arma\+::sp\+\_\+mat). \\ \hline \end{DoxyTemplParams} \begin{DoxyParams}{Parameters} {\em data} & Dataset to cluster. \\ \hline {\em assignments} & Vector to store cluster assignments. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1dbscan_1_1DBSCAN_ac413cbc512ae367a3824f70b0b497f4e}} \index{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN@{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN}!Cluster@{Cluster}} \index{Cluster@{Cluster}!mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN@{mlpack\+::dbscan\+::\+D\+B\+S\+C\+AN}} \subsubsection{Cluster()\hspace{0.1cm}{\footnotesize\ttfamily [3/3]}} {\footnotesize\ttfamily size\+\_\+t Cluster (\begin{DoxyParamCaption}\item[{const Mat\+Type \&}]{data, }\item[{arma\+::\+Row$<$ size\+\_\+t $>$ \&}]{assignments, }\item[{arma\+::mat \&}]{centroids }\end{DoxyParamCaption})} Performs \doxyref{D\+B\+S\+C\+AN}{p.}{classmlpack_1_1dbscan_1_1DBSCAN} clustering on the data, returning number of clusters, the centroid of each cluster and also the list of cluster assignments. If assignments[i] == S\+I\+Z\+E\+\_\+\+M\+AX, then the point is considered \char`\"{}noise\char`\"{}. \begin{DoxyTemplParams}{Template Parameters} {\em Mat\+Type} & Type of matrix (arma\+::mat or arma\+::sp\+\_\+mat). \\ \hline \end{DoxyTemplParams} \begin{DoxyParams}{Parameters} {\em data} & Dataset to cluster. \\ \hline {\em assignments} & Vector to store cluster assignments. \\ \hline {\em centroids} & Matrix in which centroids are stored. \\ \hline \end{DoxyParams} The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize} \item /var/www/mlpack.\+ratml.\+org/mlpack.\+org/\+\_\+src/mlpack-\/3.\+3.\+0/src/mlpack/methods/dbscan/\textbf{ dbscan.\+hpp}\end{DoxyCompactItemize}