• SAS/STAT

  • Referenced in 438 articles [sw18788]
  • variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, and nonparametric...
  • ROCK

  • Referenced in 72 articles [sw37720]
  • robust clustering algorithm for categorical attributes. Clustering, in data mining, is useful to discover distribution ... paper, we study clustering algorithms for data with boolean and categorical attributes. We show that ... between points for clustering are not appropriate for boolean and categorical attributes. Instead, we propose ... synthetic data sets to demonstrate the effectiveness of our techniques. For data with categorical attributes...
  • COOLCAT

  • Referenced in 30 articles [sw37383]
  • paper we explore the connection between clustering categorical data and entropy: clusters of similar ... capable of efficiently clustering large data sets of records with categorical attributes, and data streams ... with other categorical clustering algorithms published in the past, COOLCAT’s clustering results are very ... well equipped to deal with clustering of data streams(continuously arriving streams of data point...
  • MULTIMIX

  • Referenced in 34 articles [sw03250]
  • program is designed to cluster multivariate data that have categorical and continuous variables and that ... example, the program is used to cluster a large medical dataset...
  • clustrd

  • Referenced in 5 articles [sw17529]
  • clustering of continuous or categorical data. For continuous data, the package contains implementations of factorial ... component analysis with K-means clustering. For categorical data, the package provides MCA K-means ... href=”http://dx.doi.org/10.1007/s00180-012-0329-x”>doi:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D’Enza...
  • blockcluster

  • Referenced in 10 articles [sw17883]
  • Package for Binary, Categorical, Contingency and Continuous Data-Sets. Simultaneous clustering of rows and columns ... important technique in two way data analysis. It consists of estimating a mixture model which ... takes into account the block clustering problem on both the individual and variables sets ... package allows to co-cluster binary, contingency, continuous and categorical data-sets. It also provides...
  • CoModes

  • Referenced in 2 articles [sw31378]
  • package CoModes: a package for clustering categorical data. CoModes is a tool for clustering categorical...
  • SpectralCAT

  • Referenced in 8 articles [sw18794]
  • SpectralCAT: Categorical spectral clustering of numerical and nominal data. Data clustering is a common technique ... technique, called SpectralCAT, for unsupervised clustering of high-dimensional data that contains numerical or nominal ... automatically transform the high-dimensional input data into categorical values. This is done by discovering ... method for spectral clustering via dimensionality reduction of the transformed data is applied. This...
  • ECCLAT

  • Referenced in 2 articles [sw02548]
  • ECCLAT: a new approach of clusters discovery in categorical data. We present a new approach ... discovery of meaningful clusters from large categorical data (which is an usual situation ... method called ECCLAT (for Extraction of Clusters from Concepts LATtice) extracts a subset of concepts...
  • PReMiuM

  • Referenced in 13 articles [sw14746]
  • data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response...
  • CL.E.KMODES

  • Referenced in 1 article [sw02751]
  • present a new method for clustering categorical data sets named CL.E.KMODES. The proposed method ... more accurate way of assigning objects to clusters. In particular, it compares each object with...
  • DIVCLUS-T

  • Referenced in 5 articles [sw02736]
  • method. DIVCLUS-T is a divisive hierarchical clustering algorithm based on a monothetic bipartitional approach ... either numerical or categorical data. Like the Ward agglomerative hierarchical clustering algorithm...
  • clustMixType

  • Referenced in 3 articles [sw16328]
  • Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery...
  • kamila

  • Referenced in 3 articles [sw16426]
  • methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention ... categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data ... rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force ... maximize the cluster separation simultaneously in the continuous and categorical variables...
  • jomo

  • Referenced in 6 articles [sw19482]
  • categorical data through latent normal variables and the option to use cluster-specific covariance matrices...
  • LCAvarsel

  • Referenced in 1 article [sw24497]
  • class analysis for model-based clustering of multivariate categorical data. The package implements a general ... selecting the subset of variables with relevant clustering information and discard those that are redundant...
  • PyClustering

  • Referenced in 6 articles [sw30859]
  • exponential growth in their data volumes, and so automatic categorization techniques have become standard tools ... dataset exploration. Automatic categorization techniques – typically referred to as clustering – help expose the structure ... dataset. For example, the generated clusters might each correspond to a customer group with reasonably ... invented new clustering techniques. PyClustering is an open source data mining library written in Python...
  • Equi-Clustream

  • Referenced in 1 article [sw26827]
  • time. The problem of clustering time-evolving metric data and categorical time-evolving data ... problem of clustering mixed type time-evolving data remains a challenging issue ... between the structure of metric and categorical attributes. In this paper, we devise a generalized ... Equi-Clustream to dynamically cluster mixed type time-evolving data, which comprises three algorithms...
  • greed

  • Referenced in 2 articles [sw41875]
  • enable the clustering of networks and data matrices (such as counts, categorical or continuous) with ... type of generative models. Model selection and clustering is performed in combination by optimizing...
  • MacSpin

  • Referenced in 11 articles [sw14078]
  • visually find trends, clusters, and other patterns in multivariate data as well as highly unusual ... data manipulation and calculation features that allow the user to transform, edit, and categorize data...