zero-one.geni.ml.clustering
bisecting-k-meansclj
(bisecting-k-means params)A bisecting k-means algorithm based on the paper "A comparison of document clustering techniques" by Steinbach, Karypis, and Kumar, with modification to fit Spark. The algorithm starts from a single cluster that contains all points. Iteratively it finds divisible clusters on the bottom level and bisects each of them using k-means, until there are k leaf clusters in total or no leaf clusters are divisible. The bisecting steps of clusters on the same level are grouped together to increase parallelism. If bisecting all divisible clusters on the bottom level would result more than k leaf clusters, larger clusters get higher priority.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/BisectingKMeans.html
Timestamp: 2020-10-19T01:56:03.281Z
A bisecting k-means algorithm based on the paper "A comparison of document clustering techniques" by Steinbach, Karypis, and Kumar, with modification to fit Spark. The algorithm starts from a single cluster that contains all points. Iteratively it finds divisible clusters on the bottom level and bisects each of them using k-means, until there are k leaf clusters in total or no leaf clusters are divisible. The bisecting steps of clusters on the same level are grouped together to increase parallelism. If bisecting all divisible clusters on the bottom level would result more than k leaf clusters, larger clusters get higher priority. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/BisectingKMeans.html Timestamp: 2020-10-19T01:56:03.281Z
gaussian-mixtureclj
(gaussian-mixture params)Gaussian Mixture clustering.
This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs). A GMM represents a composite distribution of independent Gaussian distributions with associated "mixing" weights specifying each's contribution to the composite.
Given a set of sample points, this class will maximize the log-likelihood for a mixture of k Gaussians, iterating until the log-likelihood changes by less than convergenceTol, or until it has reached the max number of iterations. While this process is generally guaranteed to converge, it is not guaranteed to find a global optimum.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/GaussianMixture.html
Timestamp: 2020-10-19T01:56:03.645Z
Gaussian Mixture clustering. This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs). A GMM represents a composite distribution of independent Gaussian distributions with associated "mixing" weights specifying each's contribution to the composite. Given a set of sample points, this class will maximize the log-likelihood for a mixture of k Gaussians, iterating until the log-likelihood changes by less than convergenceTol, or until it has reached the max number of iterations. While this process is generally guaranteed to converge, it is not guaranteed to find a global optimum. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/GaussianMixture.html Timestamp: 2020-10-19T01:56:03.645Z
gmmclj
(gmm params)Gaussian Mixture clustering.
This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs). A GMM represents a composite distribution of independent Gaussian distributions with associated "mixing" weights specifying each's contribution to the composite.
Given a set of sample points, this class will maximize the log-likelihood for a mixture of k Gaussians, iterating until the log-likelihood changes by less than convergenceTol, or until it has reached the max number of iterations. While this process is generally guaranteed to converge, it is not guaranteed to find a global optimum.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/GaussianMixture.html
Timestamp: 2020-10-19T01:56:03.645Z
Gaussian Mixture clustering. This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs). A GMM represents a composite distribution of independent Gaussian distributions with associated "mixing" weights specifying each's contribution to the composite. Given a set of sample points, this class will maximize the log-likelihood for a mixture of k Gaussians, iterating until the log-likelihood changes by less than convergenceTol, or until it has reached the max number of iterations. While this process is generally guaranteed to converge, it is not guaranteed to find a global optimum. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/GaussianMixture.html Timestamp: 2020-10-19T01:56:03.645Z
k-meansclj
(k-means params)K-means clustering with support for k-means|| initialization proposed by Bahmani et al.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/KMeans.html
Timestamp: 2020-10-19T01:56:04.224Z
K-means clustering with support for k-means|| initialization proposed by Bahmani et al. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/KMeans.html Timestamp: 2020-10-19T01:56:04.224Z
latent-dirichlet-allocationclj
(latent-dirichlet-allocation params)Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
Terminology:
Original LDA paper (journal version): Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003.
Input data (featuresCol): LDA is given a collection of documents as input data, via the featuresCol parameter. Each document is specified as a Vector of length vocabSize, where each entry is the count for the corresponding term (word) in the document. Feature transformers such as org.apache.spark.ml.feature.Tokenizer and org.apache.spark.ml.feature.CountVectorizer can be useful for converting text to word count vectors.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/LDA.html
Timestamp: 2020-10-19T01:56:04.609Z
Latent Dirichlet Allocation (LDA), a topic model designed for text documents. Terminology: Original LDA paper (journal version): Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003. Input data (featuresCol): LDA is given a collection of documents as input data, via the featuresCol parameter. Each document is specified as a Vector of length vocabSize, where each entry is the count for the corresponding term (word) in the document. Feature transformers such as org.apache.spark.ml.feature.Tokenizer and org.apache.spark.ml.feature.CountVectorizer can be useful for converting text to word count vectors. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/LDA.html Timestamp: 2020-10-19T01:56:04.609Z
ldaclj
(lda params)Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
Terminology:
Original LDA paper (journal version): Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003.
Input data (featuresCol): LDA is given a collection of documents as input data, via the featuresCol parameter. Each document is specified as a Vector of length vocabSize, where each entry is the count for the corresponding term (word) in the document. Feature transformers such as org.apache.spark.ml.feature.Tokenizer and org.apache.spark.ml.feature.CountVectorizer can be useful for converting text to word count vectors.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/LDA.html
Timestamp: 2020-10-19T01:56:04.609Z
Latent Dirichlet Allocation (LDA), a topic model designed for text documents. Terminology: Original LDA paper (journal version): Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003. Input data (featuresCol): LDA is given a collection of documents as input data, via the featuresCol parameter. Each document is specified as a Vector of length vocabSize, where each entry is the count for the corresponding term (word) in the document. Feature transformers such as org.apache.spark.ml.feature.Tokenizer and org.apache.spark.ml.feature.CountVectorizer can be useful for converting text to word count vectors. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/LDA.html Timestamp: 2020-10-19T01:56:04.609Z
power-iteration-clusteringclj
(power-iteration-clustering params)Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by Lin and Cohen. From the abstract: PIC finds a very low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise similarity matrix of the data.
This class is not yet an Estimator/Transformer, use assignClusters method to run the PowerIterationClustering algorithm.
Timestamp: 2020-10-19T01:56:04.968Z
Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by Lin and Cohen. From the abstract: PIC finds a very low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise similarity matrix of the data. This class is not yet an Estimator/Transformer, use assignClusters method to run the PowerIterationClustering algorithm. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/clustering/PowerIterationClustering.html Timestamp: 2020-10-19T01:56:04.968Z
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