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.
Model fitted by BisectingKMeans.
Model fitted by BisectingKMeans.
:: Experimental :: Summary of BisectingKMeans.
:: Experimental :: Summary of BisectingKMeans.
:: Experimental :: Summary of clustering algorithms.
:: Experimental :: Summary of clustering algorithms.
Distributed model fitted by LDA.
Distributed model fitted by LDA. This type of model is currently only produced by Expectation-Maximization (EM).
This model stores the inferred topics, the full training dataset, and the topic distribution for each training document.
Gaussian Mixture clustering.
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.
This algorithm is limited in its number of features since it requires storing a covariance matrix which has size quadratic in the number of features. Even when the number of features does not exceed this limit, this algorithm may perform poorly on high-dimensional data. This is due to high-dimensional data (a) making it difficult to cluster at all (based on statistical/theoretical arguments) and (b) numerical issues with Gaussian distributions.
Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points are drawn from each Gaussian i with probability weights(i).
Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points are drawn from each Gaussian i with probability weights(i).
:: Experimental :: Summary of GaussianMixture.
:: Experimental :: Summary of GaussianMixture.
K-means clustering with support for k-means|| initialization proposed by Bahmani et al.
K-means clustering with support for k-means|| initialization proposed by Bahmani et al.
Model fitted by KMeans.
Model fitted by KMeans.
:: Experimental :: Summary of KMeans.
:: Experimental :: Summary of KMeans.
Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
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.
Model fitted by LDA.
Model fitted by LDA.
Local (non-distributed) model fitted by LDA.
Local (non-distributed) model fitted by LDA.
This model stores the inferred topics only; it does not store info about the training dataset.
:: Experimental :: Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by Lin and Cohen.
:: Experimental :: 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.
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 thank
leaf clusters, larger clusters get higher priority.Steinbach, Karypis, and Kumar, A comparison of document clustering techniques, KDD Workshop on Text Mining, 2000.