{"title":"MDS","nodeType":"Learner","id":"*SCgPuzvfM-9t325D","factoryName":"org.knime.base.node.mine.mds.MDSNodeFactory","factoryId":"org.knime.base.node.mine.mds.MDSNodeFactory","path":["Analytics","Mining","MDS"],"owner":"knime","ownerAccountId":"account:team:b5f0848f-496d-4e6a-b869-fa3532248db7","description":"<p>This node maps data of a high dimensional space onto a lower (usually 2 or 3) dimensional space. Therefore the Sammons mapping is applied, which iteratively decreases the difference of the distances of high and low dimensional data. Each original data point is represented by a data point of a lower dimension. The Sammons mapping tries to keep the distance information of the high dimensional data by adjusting the low dimensional data points in a certain way. Each low dimensional data point is moved around a bit towards or back from the other points accordant to its high dimensional distances. This procedure is repeated a specified number of epochs or iterations respectively.</p>\n        ","shortDescription":"Multi dimensional scaling node, mapping data of a high dimensional space onto a lower dimensional space\n    by applying the Sammons mapping.","keywords":[],"deprecated":false,"streamable":false,"tags":["Analytics","Mining","MDS"],"icon":"data:image/png;base64,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","views":[],"inPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Any input table","description":"Data table containing the data to map."}],"outPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"The input data and the mapped data","description":"The input data and the mapped data."}],"dynInPorts":[],"dynOutPorts":[],"additionalSiteInformation":{"url":"https://update.knime.com/analytics-platform/5.12","trusted":true,"enabledByDefault":true,"name":"KNIME Analytics Platform 5.12 Update Site","version":"5.12.0"},"dialog":[{"sectionName":"Options","fields":[{"name":"Epochs","description":"Specifies the number of epochs to train.","optional":false},{"name":"Output dimensions","description":"Specifies the dimension of the mapped output data.","optional":false},{"name":"Learning rate","description":"Specifies the learning rate to use. The learning rate is decreased automatically over the trained epochs.","optional":false},{"name":"Random seed","description":"Specifies the random seed to use, which allows to reproduce a mapping even if the initialization is done randomly.","optional":false},{"name":"Distance metric","description":"Select the distance metric to use for computing distances between data points.\n<ul><li><b>Euclidean</b> : The straight-line distance between two points in n-dimensional space (L2 norm).</li><li><b>Manhattan</b> : The sum of absolute differences between coordinates (L1 norm).</li></ul>","optional":false},{"name":"Use all rows","description":"If enabled, all rows in the input table will be used for the MDS computation. If disabled, only the specified number of rows will be used.","optional":false},{"name":"Number of rows to use","description":"Specifies the number of rows to apply the MDS on.","optional":false},{"name":"Always include all columns","description":"If checked, node behaves as if all columns were moved to the &#34;Include&#34; list.","optional":false},{"name":"Columns to project","description":"Specifies the columns to use by the mapping.","optional":false}]}],"links":[],"kudos":[],"kudosCount":0,"bundleInformation":{"bundleVersion":"5.12.0.v202606261225","bundleName":"KNIME Base Nodes","bundleSymbolicName":"org.knime.base","bundleVendor":"KNIME AG, Zurich, Switzerland","featureSymbolicName":"org.knime.features.base","featureName":"KNIME Base nodes","featureVersion":"5.12.0.v202606261225","featureVendor":"KNIME AG, Zurich, Switzerland","extensionId":"*bjR3r1yWOznPIEXS"},"frequentSuccessors":[{"id":"*xg4kORa5IVFVfpPN","factoryName":"org.knime.base.node.viz.plotter.scatter.ScatterPlotterNodeFactory","probability":0.14084507042253522},{"id":"*OQlWyEt6N59f0jmF","factoryName":"org.knime.base.node.viz.property.color.ColorManager2NodeFactory","probability":0.08450704225352113},{"id":"*DOkyMaii62U05xZ1","factoryName":"org.knime.base.node.preproc.filter.column.DataColumnSpecFilterNodeFactory","probability":0.056338028169014086},{"id":"*HnJl14W4MjyvH1Zk","factoryName":"org.knime.js.base.node.viz.plotter.scatterSelectionAppender.ScatterPlotNodeFactory","probability":0.056338028169014086},{"id":"*T6Ws0R0oT8Zc97RD","factoryName":"org.erlwood.knime.nodes.graph.NGraphNodeFactory","probability":0.04225352112676056},{"id":"*oluxz9j9RhfJY3E0","factoryName":"org.knime.base.node.preproc.correlation.compute.CorrelationComputeNodeFactory","probability":0.04225352112676056},{"id":"*EeJsApVFGjfKslVY","factoryName":"org.knime.base.node.mine.mds.mdsprojection.MDSProjectionNodeFactory","probability":0.028169014084507043},{"id":"*ZgYyOm1P9VJVBE9e","factoryName":"org.knime.base.node.preproc.joiner.Joiner2NodeFactory","probability":0.028169014084507043},{"id":"*gwwIZ5c5EYsJErci","factoryName":"org.knime.base.node.preproc.sorter.SorterNodeFactory","probability":0.028169014084507043},{"id":"*9iAY3thoKzyWdyEf","factoryName":"org.knime.ext.jfc.node.linechart.JfcLineChartColoredNodeFactory","probability":0.014084507042253521}]}