A generalized multi-aspect distance metric for mixed-type data clustering

Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes’ different n...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Mousavi, Elahe [verfasserIn] Sehhati, Mohammadreza [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Clustering Mixed data Ordinal and nominal attribute Inter-dependency Intra-attribute information Mutual information

Übergeordnetes Werk:	Enthalten in: Pattern recognition - Amsterdam : Elsevier, 1968, 138
Übergeordnetes Werk:	volume:138

DOI / URN:	10.1016/j.patcog.2023.109353

Katalog-ID:	ELV063046482

Internformat


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245	1	0	\|a A generalized multi-aspect distance metric for mixed-type data clustering
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520			\|a Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes’ different natures. In this study, we proposed a new measure of distance for a mixed-type data set that regards inter-attribute information and intra-attribute information depending on the type of attributes. In this regard, entropy and Jensen–Shannon divergence concepts were used to exploit the inter-attribute information of categorical-categorical and categorical-numerical attributes, respectively. Also, a modified version of Mahalanobis distance was proposed to consider the intra- and inter-attribute information of numerical attributes. We also introduced a unified framework based on mutual information to control attributes’ contribution to distance measurement. The proposed distance in conjunction with spectral clustering was extensively evaluated concerning various categorical, numerical, and mixed-type benchmark data sets, and the results demonstrated the efficacy of the proposed method.
650		4	\|a Clustering
650		4	\|a Mixed data
650		4	\|a Ordinal and nominal attribute
650		4	\|a Inter-dependency
650		4	\|a Intra-attribute information
650		4	\|a Mutual information
700	1		\|a Sehhati, Mohammadreza \|e verfasserin \|0 (orcid)0000-0002-7316-5645 \|4 aut
773	0	8	\|i Enthalten in \|t Pattern recognition \|d Amsterdam : Elsevier, 1968 \|g 138 \|h Online-Ressource \|w (DE-627)265784131 \|w (DE-600)1466343-0 \|w (DE-576)101177364 \|7 nnns
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912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
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author_variant	e m em m s ms
matchkey_str	mousavielahesehhatimohammadreza:2023----:gnrlzdutapcdsacmtifrie
hierarchy_sort_str	2023
bklnumber	54.74
publishDate	2023
allfields	10.1016/j.patcog.2023.109353 doi (DE-627)ELV063046482 (ELSEVIER)S0031-3203(23)00054-7 DE-627 ger DE-627 rda eng 000 150 VZ 54.74 bkl Mousavi, Elahe verfasserin aut A generalized multi-aspect distance metric for mixed-type data clustering 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes’ different natures. In this study, we proposed a new measure of distance for a mixed-type data set that regards inter-attribute information and intra-attribute information depending on the type of attributes. In this regard, entropy and Jensen–Shannon divergence concepts were used to exploit the inter-attribute information of categorical-categorical and categorical-numerical attributes, respectively. Also, a modified version of Mahalanobis distance was proposed to consider the intra- and inter-attribute information of numerical attributes. We also introduced a unified framework based on mutual information to control attributes’ contribution to distance measurement. The proposed distance in conjunction with spectral clustering was extensively evaluated concerning various categorical, numerical, and mixed-type benchmark data sets, and the results demonstrated the efficacy of the proposed method. Clustering Mixed data Ordinal and nominal attribute Inter-dependency Intra-attribute information Mutual information Sehhati, Mohammadreza verfasserin (orcid)0000-0002-7316-5645 aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 138 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:138 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 138
spelling	10.1016/j.patcog.2023.109353 doi (DE-627)ELV063046482 (ELSEVIER)S0031-3203(23)00054-7 DE-627 ger DE-627 rda eng 000 150 VZ 54.74 bkl Mousavi, Elahe verfasserin aut A generalized multi-aspect distance metric for mixed-type data clustering 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes’ different natures. In this study, we proposed a new measure of distance for a mixed-type data set that regards inter-attribute information and intra-attribute information depending on the type of attributes. In this regard, entropy and Jensen–Shannon divergence concepts were used to exploit the inter-attribute information of categorical-categorical and categorical-numerical attributes, respectively. Also, a modified version of Mahalanobis distance was proposed to consider the intra- and inter-attribute information of numerical attributes. We also introduced a unified framework based on mutual information to control attributes’ contribution to distance measurement. The proposed distance in conjunction with spectral clustering was extensively evaluated concerning various categorical, numerical, and mixed-type benchmark data sets, and the results demonstrated the efficacy of the proposed method. Clustering Mixed data Ordinal and nominal attribute Inter-dependency Intra-attribute information Mutual information Sehhati, Mohammadreza verfasserin (orcid)0000-0002-7316-5645 aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 138 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:138 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 138
allfields_unstemmed	10.1016/j.patcog.2023.109353 doi (DE-627)ELV063046482 (ELSEVIER)S0031-3203(23)00054-7 DE-627 ger DE-627 rda eng 000 150 VZ 54.74 bkl Mousavi, Elahe verfasserin aut A generalized multi-aspect distance metric for mixed-type data clustering 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes’ different natures. In this study, we proposed a new measure of distance for a mixed-type data set that regards inter-attribute information and intra-attribute information depending on the type of attributes. In this regard, entropy and Jensen–Shannon divergence concepts were used to exploit the inter-attribute information of categorical-categorical and categorical-numerical attributes, respectively. Also, a modified version of Mahalanobis distance was proposed to consider the intra- and inter-attribute information of numerical attributes. We also introduced a unified framework based on mutual information to control attributes’ contribution to distance measurement. The proposed distance in conjunction with spectral clustering was extensively evaluated concerning various categorical, numerical, and mixed-type benchmark data sets, and the results demonstrated the efficacy of the proposed method. Clustering Mixed data Ordinal and nominal attribute Inter-dependency Intra-attribute information Mutual information Sehhati, Mohammadreza verfasserin (orcid)0000-0002-7316-5645 aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 138 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:138 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 138
allfieldsGer	10.1016/j.patcog.2023.109353 doi (DE-627)ELV063046482 (ELSEVIER)S0031-3203(23)00054-7 DE-627 ger DE-627 rda eng 000 150 VZ 54.74 bkl Mousavi, Elahe verfasserin aut A generalized multi-aspect distance metric for mixed-type data clustering 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes’ different natures. In this study, we proposed a new measure of distance for a mixed-type data set that regards inter-attribute information and intra-attribute information depending on the type of attributes. In this regard, entropy and Jensen–Shannon divergence concepts were used to exploit the inter-attribute information of categorical-categorical and categorical-numerical attributes, respectively. Also, a modified version of Mahalanobis distance was proposed to consider the intra- and inter-attribute information of numerical attributes. We also introduced a unified framework based on mutual information to control attributes’ contribution to distance measurement. The proposed distance in conjunction with spectral clustering was extensively evaluated concerning various categorical, numerical, and mixed-type benchmark data sets, and the results demonstrated the efficacy of the proposed method. Clustering Mixed data Ordinal and nominal attribute Inter-dependency Intra-attribute information Mutual information Sehhati, Mohammadreza verfasserin (orcid)0000-0002-7316-5645 aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 138 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:138 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 138
allfieldsSound	10.1016/j.patcog.2023.109353 doi (DE-627)ELV063046482 (ELSEVIER)S0031-3203(23)00054-7 DE-627 ger DE-627 rda eng 000 150 VZ 54.74 bkl Mousavi, Elahe verfasserin aut A generalized multi-aspect distance metric for mixed-type data clustering 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes’ different natures. In this study, we proposed a new measure of distance for a mixed-type data set that regards inter-attribute information and intra-attribute information depending on the type of attributes. In this regard, entropy and Jensen–Shannon divergence concepts were used to exploit the inter-attribute information of categorical-categorical and categorical-numerical attributes, respectively. Also, a modified version of Mahalanobis distance was proposed to consider the intra- and inter-attribute information of numerical attributes. We also introduced a unified framework based on mutual information to control attributes’ contribution to distance measurement. The proposed distance in conjunction with spectral clustering was extensively evaluated concerning various categorical, numerical, and mixed-type benchmark data sets, and the results demonstrated the efficacy of the proposed method. Clustering Mixed data Ordinal and nominal attribute Inter-dependency Intra-attribute information Mutual information Sehhati, Mohammadreza verfasserin (orcid)0000-0002-7316-5645 aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 138 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:138 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 138
language	English
source	Enthalten in Pattern recognition 138 volume:138
sourceStr	Enthalten in Pattern recognition 138 volume:138
format_phy_str_mv	Article
bklname	Maschinelles Sehen
institution	findex.gbv.de
topic_facet	Clustering Mixed data Ordinal and nominal attribute Inter-dependency Intra-attribute information Mutual information
dewey-raw	000
isfreeaccess_bool	false
container_title	Pattern recognition
authorswithroles_txt_mv	Mousavi, Elahe @@aut@@ Sehhati, Mohammadreza @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	265784131
dewey-sort	0
id	ELV063046482
language_de	englisch
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author	Mousavi, Elahe
spellingShingle	Mousavi, Elahe ddc 000 bkl 54.74 misc Clustering misc Mixed data misc Ordinal and nominal attribute misc Inter-dependency misc Intra-attribute information misc Mutual information A generalized multi-aspect distance metric for mixed-type data clustering
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topic_title	000 150 VZ 54.74 bkl A generalized multi-aspect distance metric for mixed-type data clustering Clustering Mixed data Ordinal and nominal attribute Inter-dependency Intra-attribute information Mutual information
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title	A generalized multi-aspect distance metric for mixed-type data clustering
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title_sort	a generalized multi-aspect distance metric for mixed-type data clustering
title_auth	A generalized multi-aspect distance metric for mixed-type data clustering
abstract	Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes’ different natures. In this study, we proposed a new measure of distance for a mixed-type data set that regards inter-attribute information and intra-attribute information depending on the type of attributes. In this regard, entropy and Jensen–Shannon divergence concepts were used to exploit the inter-attribute information of categorical-categorical and categorical-numerical attributes, respectively. Also, a modified version of Mahalanobis distance was proposed to consider the intra- and inter-attribute information of numerical attributes. We also introduced a unified framework based on mutual information to control attributes’ contribution to distance measurement. The proposed distance in conjunction with spectral clustering was extensively evaluated concerning various categorical, numerical, and mixed-type benchmark data sets, and the results demonstrated the efficacy of the proposed method.
abstractGer	Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes’ different natures. In this study, we proposed a new measure of distance for a mixed-type data set that regards inter-attribute information and intra-attribute information depending on the type of attributes. In this regard, entropy and Jensen–Shannon divergence concepts were used to exploit the inter-attribute information of categorical-categorical and categorical-numerical attributes, respectively. Also, a modified version of Mahalanobis distance was proposed to consider the intra- and inter-attribute information of numerical attributes. We also introduced a unified framework based on mutual information to control attributes’ contribution to distance measurement. The proposed distance in conjunction with spectral clustering was extensively evaluated concerning various categorical, numerical, and mixed-type benchmark data sets, and the results demonstrated the efficacy of the proposed method.
abstract_unstemmed	Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes’ different natures. In this study, we proposed a new measure of distance for a mixed-type data set that regards inter-attribute information and intra-attribute information depending on the type of attributes. In this regard, entropy and Jensen–Shannon divergence concepts were used to exploit the inter-attribute information of categorical-categorical and categorical-numerical attributes, respectively. Also, a modified version of Mahalanobis distance was proposed to consider the intra- and inter-attribute information of numerical attributes. We also introduced a unified framework based on mutual information to control attributes’ contribution to distance measurement. The proposed distance in conjunction with spectral clustering was extensively evaluated concerning various categorical, numerical, and mixed-type benchmark data sets, and the results demonstrated the efficacy of the proposed method.
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title_short	A generalized multi-aspect distance metric for mixed-type data clustering
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A generalized multi-aspect distance metric for mixed-type data clustering

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