{"title":"Fuzzy c-Means","nodeType":"Learner","id":"*eKnxs53FkU9CuBKg","factoryName":"org.knime.base.node.mine.cluster.fuzzycmeans.FuzzyClusterNodeFactory2","factoryId":"org.knime.base.node.mine.cluster.fuzzycmeans.FuzzyClusterNodeFactory2","path":["Analytics","Mining","Clustering"],"owner":"knime","ownerAccountId":"account:team:b5f0848f-496d-4e6a-b869-fa3532248db7","description":"<p>The fuzzy c-means algorithm is a well-known unsupervised learning technique that can be used to reveal the underlying structure of the data. Fuzzy clustering allows each data point to belong to several clusters, with a degree of membership to each one.\n <br />\n <b>Make sure that the input data is normalized to obtain better clustering results.</b>\n <br />\n The first output datatable provides the original datatable with the cluster memberships to each cluster. The second datatable provides the values of the cluster prototypes.\n <br />\n Additionally, it is possible to induce a noise cluster, to detect noise in the dataset, based on the approach from R. N. Dave: &#39;Characterization and detection of noise in clustering&#39;.</p>\n            \n        \n        ","shortDescription":"Performs fuzzy c-means clustering.","keywords":[],"deprecated":false,"streamable":false,"tags":["Analytics","Mining","Clustering"],"icon":"data:image/png;base64,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","views":[{"name":"Statistics View","description":"Shows the WithinClusterVariation and the BetweenClusterVariation, which are indicators for &#39;good&#39; clustering."}],"inPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Training data","description":"Datatable with training data. Make sure that the data are normalized!"}],"outPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Cluster Memberships","description":"Input table extended by cluster membership"},{"objectClass":"org.knime.core.node.port.pmml.PMMLPortObject","color":"1469af","dataType":"PMML","optional":false,"name":"Prototypes","description":"Cluster centers"}],"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":"Number of clusters","description":"Number of clusters to use for the algorithm.","optional":false},{"name":"Maximum number of iterations","description":"This is the maximum number of iterations to be performed.","optional":false},{"name":"Fuzzifier","description":"Indicates how much the clusters are allowed to overlap.","optional":false},{"name":"Use seed for random initialization","description":"If this checkbox is set, a seed can be set for initializing the cluster prototypes.","optional":false},{"name":"Random seed","description":"The seed value used for random number generation. Use the same seed to get identical clustering results across multiple executions.","optional":false},{"name":"Draw seed","description":"Generate a random seed and set it in the Random seed input above for reproducible runs.","optional":false},{"name":"Always include all columns","description":"If checked, node behaves as if all columns were moved to the Include list.","optional":false},{"name":"Used attributes","description":"Select the columns that are to be used for clustering. Make sure that the input data is normalized to obtain better clustering results.","optional":false},{"name":"Induce noise cluster","description":"Whether to induce a noise cluster or not.","optional":false},{"name":"Delta computation method","description":"Choose whether to set a fixed delta value or to compute it automatically based on lambda.\n<ul><li><b>Automatic delta, specify lambda</b> : Delta is updated in each iteration, based on the average interpoint distances. However, a lambda paramater has to be set, according to the shape of the clusters.</li><li><b>Set delta</b> : Delta is the fixed distance from every datapoint to the noise cluster.</li></ul>","optional":false},{"name":"Delta","description":"The delta value.","optional":false},{"name":"Lambda","description":"The lambda value.","optional":false},{"name":"Perform the clustering in memory","description":"If this option is selected, the clustering is performed in the memory, which speeds up the process.","optional":false},{"name":"Compute cluster quality measures","description":"Whether to calculate quality measures for the clustering. This can be time and memory consuming with large datasets.","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":"*OQlWyEt6N59f0jmF","factoryName":"org.knime.base.node.viz.property.color.ColorManager2NodeFactory","probability":0.096875},{"id":"*9kg2rX7d5j2FrgtM","factoryName":"org.knime.base.node.mine.cluster.assign.ClusterAssignerNodeFactory","probability":0.08125},{"id":"*ZgYyOm1P9VJVBE9e","factoryName":"org.knime.base.node.preproc.joiner.Joiner2NodeFactory","probability":0.046875},{"id":"*5stmXk6zY_ORA4bC","factoryName":"org.knime.base.node.preproc.groupby.GroupByNodeFactory","probability":0.0375},{"id":"*xg4kORa5IVFVfpPN","factoryName":"org.knime.base.node.viz.plotter.scatter.ScatterPlotterNodeFactory","probability":0.0375},{"id":"*ViVacy0amF1ou_Oq","factoryName":"org.knime.base.node.mine.scorer.entrop.NewEntropyNodeFactory","probability":0.034375},{"id":"*jd2U-FYTI8jCbQls","factoryName":"org.knime.base.node.preproc.filter.row.RowFilterNodeFactory","probability":0.03125},{"id":"*DOkyMaii62U05xZ1","factoryName":"org.knime.base.node.preproc.filter.column.DataColumnSpecFilterNodeFactory","probability":0.03125},{"id":"*PHWv6FsABEjrSc76","factoryName":"org.knime.base.node.preproc.rename.RenameNodeFactory","probability":0.025},{"id":"*XSCKH1I1S2QiA5_-","factoryName":"org.knime.base.node.preproc.normalize.NormalizerDeNodeFactory","probability":0.025}]}