Java example source code file (ml.xml)
The ml.xml Java example source code
<?xml version="1.0"?> <!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <document url="ml.html"> <properties> <title>The Commons Math User Guide - Machine Learning </properties> <body> <section name="16 Machine Learning"> <subsection name="16.1 Overview" href="overview"> <p> Machine learning support in commons-math currently provides operations to cluster data sets based on a distance measure. </p> </subsection> <subsection name="16.2 Clustering algorithms and distance measures" href="clustering"> <p> The <a href="../apidocs/org/apache/commons/math3/ml/clustering/Clusterer.html"> Clusterer</a> class represents a clustering algorithm. The following algorithms are available: <ul> <li>KMeans++: It is based on the well-known kMeans algorithm, but uses a different method for choosing the initial values (or "seeds") and thus avoids cases where KMeans sometimes results in poor clusterings. KMeans/KMeans++ clustering aims to partition n observations into k clusters in such that each point belongs to the cluster with the nearest center. </li> <li>Fuzzy-KMeans: A variation of the classical K-Means algorithm, with the major difference that a single data point is not uniquely assigned to a single cluster. Instead, each point i has a set of weights u<sub>ij which indicate the degree of membership to the cluster j. The fuzzy variant does not require initial values for the cluster centers and is thus more robust, although slower than the original kMeans algorithm. </li> <li>DBSCAN: Density-based spatial clustering of applications with noise (DBSCAN) finds a number of clusters starting from the estimated density distribution of corresponding nodes. The main advantages over KMeans/KMeans++ are that DBSCAN does not require the specification of an initial number of clusters and can find arbitrarily shaped clusters. </li> <li>Multi-KMeans++: Multi-KMeans++ is a meta algorithm that basically performs n runs using KMeans++ and then chooses the best clustering (i.e., the one with the lowest distance variance over all clusters) from those runs. </li> </ul> </p> <p> An comparison of the available clustering algorithms:<br/> <img src="../images/userguide/cluster_comparison.png" alt="Comparison of clustering algorithms"/> </p> </subsection> <subsection name="16.3 Distance measures" href="distance"> <p> Each clustering algorithm requires a distance measure to determine the distance between two points (either data points or cluster centers). The following distance measures are available: <ul> <li>Canberra distance <li>ChebyshevDistance distance <li>EuclideanDistance distance <li>ManhattanDistance distance <li>Earth Mover's distance </ul> </p> </subsection> <subsection name="16.3 Example" href="example"> <p> Here is an example of a clustering execution. Let us assume we have a set of locations from our domain model, where each location has a method <code>double getX() and
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