Survey of state of art of mixed-data clustering
Partitionnal clustering
Model-based
Hierarchical clustering
Algorithms
Gower's similarity Matrix
K-prototype
"Clustering large data sets with mixed numeric and categorical values"
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New cost function and distance measure
"Ak-mean clustering algorithm for mixed numeric
and categorical data"
Similarity / Weights of numeric features
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W-K-prototypes
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Frequency of features values
"Automated variable weighting
in k-means type clustering"
"K-centers algorithm for clustering mixed
type data"
data clustering algorithm for data streams with mixed numeric and categorical
``A fast density-based data stream clustering
algorithm with cluster centers self-determined for mixed data,''
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New distance
"An equi-biased k-prototypes algorithm for clustering mixed-type data"
FGKA for mixed data
"Genetic k-means clustering algorithm for
mixed numeric and categorical data sets''
Less time
Grid-based technique
An effcient grid-based k-prototypes algorithm for sustainable decision-making on spatial
objects
Ahmed and dey
Huand and al
Zhao and al.
Roy and Sharma
Jang and al.
Sangam and Om
Che and He
``Extensions to the K-means algorithm for clustering large data sets with categorical values,''
Converting mixed data
Uses polar or spherical coordinates
"Geometrical codiication for clustering
mixed categorical and numerical databases,''
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Barcelo-Rico and Jose-Luis
‘‘Mixed data cluster analysis: An illustration
using Cypriot hooked-tang weapons,’’
new Similarity measure
Philip and Ottaway
A general coefficient of similarity and some of its properties,’
Gower
A robust scalable clustering algorithm for mixed type attributes in large database environment
BIRCH
Fang and ai
‘‘BIRCH: A new data clustering algorithm and its applications,’’
Zhang an ai
SBAC (Goodall similiarity measure)
‘‘Unsupervised learning with mixed numeric and nominal data,
Li and Biwas
A new similarity index based on probability,''
Goodall
Distance hierarchy by using concept hierarchy
‘Hierarchical clustering of mixed data based on distance hierarchy
Hsu and aI
AUTOCLASS
‘‘Bayesian classification (AutoClass): Theory
and results,’’
Cheeseman and schutz
Bayesian methods
Everitt
‘A finite mixture model for the clustering of mixed mode data 1988
Use of thresholds for categorical features
Krzanowski
Extension of homogenous conditional model
‘‘Mixture separation for mixedmode data,’’
Moustaki
Latent class mixture model
‘Latent class models for mixed variables
with applications in archaeometry 2005
Browne
‘‘Model-based clustering, classification, and discriminant analysis of data with mixed type,’ 2012
Latent variable model + expectation - maximization framework
Andreopopoulos
Pseudo bayesian process with categorical clustering data to guide numerical clustering
‘‘Bi-level clustering of mixed
categorical and numerical biomedical data,’ 2006
Hunt and Jogensen
‘‘Mixture model clustering of data sets with
categorical and continuous variables,’’ 1996
Finite mixture of multivariate distributions is fitted to data
Can be applied to mixed data with mixed value
ClustMD method
‘Model based clustering for mixed data:
ClustMD,
McParcalnd and aI
McParcland and Gormely
‘‘Clustering high-dimensional mixed data to uncover subphenotypes: Joint analysis of phenotypic and genotypic data, 2017
Latent variable model
Bayesian finite mixture model
Good results
Saadoui et aI.
‘A dimensionally reduced clustering methodology for heterogeneous occupational medicine data mining,’ 2015
Projection of categorical features
Rajan
Gaussian mixture copula
‘‘Dependency clustering of mixed data with
Gaussian mixture copulas 2016
Outperforms other algorithms
Tekmulla and aI
Vine copulas for mixed data: Multi-view clustering for mixed data beyond meta-Gaussian dependencies 2017
Vine copulas and Dirichlet process of vines
Marbac and ai
‘Model-based clustering of gaussian copulas for mixed data 2017
Mixture models of Gaussian copulas
KAMILA
‘‘A semiparametric method for clustering mixed data 2016
K-means algorithm + Gaussian multionomial mixture model
Doring and aI
‘‘Fuzzy clustering of quantitative
and qualitative data,’’ 2004
Fuzzy algorithm for mixed data
Doring
Chatzis
‘A fuzzy c-means-type algorithm for clustering of data with mixed numeric and categorical attributes employing a probabilistic dissimilarity functional 2011
FCM-type clustering
algorithm for mixed data
Pathak and pal
‘‘Clustering of mixed data by integrating fuzzy,
probabilistic, and collaborative clustering framework, 2016
Fuzzy + probabilistic + collaborative clustering
Neural networks
Hsu and Lin
Visualized analysis of multivariate mixed-type data
via an extended self-organizing map 2006
Hsu and lin
Fixed SMO
Growing SMO
‘Visualized analysis of mixed numeric and
categorical data via extended self-organizing map,2012
Tai and HSU
Generalized SMO + growing SMO
‘‘Growing self-organizing map with cross insert
for mixed-type data clustering,’’ 2012
Chen and Marques
SMO based + Hamming distance
‘An extension of self-organizing maps to
categorical data,’
Problem : gives more weigth to categorical features
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Coso and aI
Mixing numerical and categorical data in a self-organizing map by means of frequency neurons,’
Modify the distane measure
Noorbehbahani et al.
‘‘An incremental mixed data clustering method using a new distance measure,
incremental mixed-data clustering using SMO
Lam and aI
Fuzzy ART + K means clustering
‘‘Clustering data of mixed categorical
and numerical type with unsupervised feature learning,
Other
Spectral clustering
SpectralCAT
‘‘Clustering mixed data based on evidence accumulation,’
Niu and aI
A coupled user clustering algorithm based on mixed data for Web-based learning
systems,
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Combination of similarity matrices