List of usage examples for weka.clusterers AbstractClusterer subclass-usage
From source file core.DBScan.java
/**
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* Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Second International Conference on Knowledge Discovery and Data Mining, 226-231, 1996.
* <p/>
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*
From source file core.OPTICS.java
/**
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* Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander: OPTICS: Ordering Points To Identify the Clustering Structure. In: ACM SIGMOD International Conference on Management of Data, 49-60, 1999.
* <p/>
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*
From source file kmeans_extend.MyKMeans.java
/** * * @author ASUS X202E */ public class MyKMeans extends AbstractClusterer {
From source file meansagnes.MyAgnes.java
/** * * @author Fiqie */ public class MyAgnes extends AbstractClusterer {
From source file myclusterer.MyAgnes.java
/** * * @author Visat */ public class MyAgnes extends AbstractClusterer implements NumberOfClustersRequestable {
From source file org.fabrelab.textkit.tools.cluster.HierarchicalClustererWithThreshold.java
/**
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* Hierarchical clustering class.
* Implements a number of classic hierarchical clustering methods.
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*
From source file Part2.HierarchicalClusterer.java
/**
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* Hierarchical clustering class.
* Implements a number of classic hierarchical clustering methods.
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*
From source file Part2.HierarchicalClustererEx.java
/**
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* Hierarchical clustering class.
* Implements a number of classic hierarchical clustering methods.
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*
From source file pk.lums.edu.sma.processing.ml.DBSCAN.DBSCAN.java
/**
* <!-- globalinfo-start --> Basic implementation of DBSCAN clustering algorithm
* that should *not* be used as a reference for runtime benchmarks: more
* sophisticated implementations exist! Clustering of new instances is not
* supported. More info:<br/>
* <br/>
From source file technion.prime.history.converters.MyDBScan.java
/**
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* Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Second International Conference on Knowledge Discovery and Data Mining, 226-231, 1996.
* <p/>
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*