{"title":"SOTA Learner","nodeType":"Learner","id":"*qSAVjsLn8P2dtSZs","factoryName":"org.knime.base.node.mine.sota.SotaLearnerNodeFactory","factoryId":"org.knime.base.node.mine.sota.SotaLearnerNodeFactory","path":["Analytics","Mining","Clustering"],"owner":"knime","ownerAccountId":"account:team:b5f0848f-496d-4e6a-b869-fa3532248db7","description":"<p>Clusters numerical and fuzzy data hierarchically with the self organizing tree algorithm and visualizes the cluster tree similarly like a dendrogram. Additionally it builds a model for class prediction of new data. The model can be loaded into the SOTA Predictor node.</p>\n            \n            <p>The SOTA Learner node has a dialog, in which you can choose the winner, ancestor and sister learning rate, to adjust the cluster representatives: with the minimal resource and variability value to stop the growing of the tree; the minimal error, to end a cycle and the distance metric (cosinus, euclidean). The node will cluster the given data hierarchically by use of the self organizing tree algorithm and will produce a cluster tree, which is visualized by the view afterwards, similar to a dendrogram. The data is also displayed and can be highlighted, as well as each cluster representative. Class information can be trained too by selecting the &#34;Use class column&#34; option for uses of class prediction by the SOTA Predictor node.</p>\n            \n            <p>For more information about the SOTA clustering see: Herrero J., Valencia A., Dopazo J.: A hierarchical unsupervised growing neural network for clustering gene expression patterns.</p>\n            \n        \n        ","shortDescription":"Clusters numerical data with SOTA.","keywords":[],"deprecated":false,"streamable":false,"tags":["Analytics","Mining","Clustering"],"icon":"data:image/png;base64,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","views":[{"name":"SOTA tree view","description":"Displays the results of the SOTA clustering."}],"inPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Any input table","description":"Datatable with integer columns to do clustering on."}],"outPorts":[{"objectClass":"org.knime.base.node.mine.sota.SotaPortObject","color":"1eb9dc","dataType":"Sota","optional":false,"name":"SOTA model","description":"The model of the trained SOTA."}],"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":"Winner learning rate","description":"Set the winners learning rate which is used to adjust the winning cluster representative to the given data.","optional":false},{"name":"Sister learning rate","description":"Set the sisters learning rate which is used to adjust the sister-cluster representative of the winner to the given data.","optional":false},{"name":"Ancestor learning rate","description":"Set the ancestors learning rate which is used to adjust the ancestor-cluster representative of the winner to the given data.","optional":false},{"name":"Stopping criterion","description":"Select which criterion is used to stop the growth of the tree.\n<ul><li><b>Resource</b> : Stops based on a resource threshold. This is the default and more efficient option.</li><li><b>Variability</b> : Stops when cluster variability falls below a threshold. This can be computationally expensive (quadratic complexity).</li></ul>","optional":false},{"name":"Minimal variability","description":"Threshold for stopping tree growth when “Variability” is selected. Lower values lead to more detailed clustering. A value of 0 means the algorithm will continue until each data point forms its own cluster. Note: Computing variability is computationally expensive for large datasets (O(n²)).","optional":false},{"name":"Minimal resource","description":"Threshold for stopping tree growth when “Resource” is selected. Lower values lead to more detailed clustering. Values close to 0 result in very fine-grained clusters, but the value must be greater than 0.","optional":false},{"name":"Minimal error","description":"Set the minimal error value to control the epochs per cycle. If the minimal error value is small, more epochs will be needed to end a cycle. This means, that all cluster representatives will be pulled closer to their data.","optional":false},{"name":"Distance metric","description":"Choose the euclidean or cosinus distance metric, to measure the distance between datapoints and cluster representatives.","optional":false},{"name":"Use hierarchical fuzzy data","description":"Check to cluster hierarchical fuzzy rules hierarchically. The column with the fuzzy rule level has to be selected in the drop down menu on the right. The input table needs at least one integer column for this option being enabled. The drop down menu will be enabled when the checkbox is checked.","optional":false},{"name":"Hierarchical level","description":"If the checkbox on the left is checked, to cluster hierarchical fuzzy rules, the column with the fuzzy rule level must be chosen by selecting it in the drop down menu. Only integer columns can be chosen for the level.","optional":false},{"name":"Use class column","description":"Check to use the class data specified by the drop down box below. The class data will be trained too for the use of class prediction by the SOTA Predictor node. The input table needs at least one string column for this option being enabled.","optional":false},{"name":"Class column","description":"Specifies the column containing the class data. Only string columns can be chosen as class columns.","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":"*ZvfPH3f-zdiY9mJQ","factoryName":"org.knime.base.node.mine.sota.predictor.SotaPredictorNodeFactory","probability":0.9791666666666666},{"id":"*UwpLkzO3YUH9ogjZ","factoryName":"org.knime.base.node.io.predictor.PredictorWriterNodeFactory","probability":0.020833333333333332}]}