MWPCA-ICURD: density-based clustering method discovering specific shape original features View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-09

AUTHORS

Qinghua Luo, Yu Peng, Junbao Li, Xiyuan Peng

ABSTRACT

Uncertain data exist in many application fields, and there are numerous recent efforts in processing uncertain data to get more reliable results, especially uncertainty processing in clustering method. However, it is one of the urgent challenges to discover clusters with specific shape features. So we present a clustering method for data with uncertainties, and it is called multivariate wavelet principal component analysis-improved clustering using references and density (MWPCA-ICURD), which utilizes feature extraction and density-based clustering. To cluster uncertain data with specific shape original features, the original features are extracted by MWPCA method, which combines digital wavelet decomposition and principal component analysis organically. Then, a density-based clustering method ICURD is explored to discover specific shape clusters. Experimental results illustrate its validation and feasibility. More... »

PAGES

2545-2556

References to SciGraph publications

  • 2008. Clustering Uncertain Data Via K-Medoids in SCALABLE UNCERTAINTY MANAGEMENT
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00521-016-2208-9

    DOI

    http://dx.doi.org/10.1007/s00521-016-2208-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1015551590


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Guilin University of Electronic Technology", 
              "id": "https://www.grid.ac/institutes/grid.440723.6", 
              "name": [
                "School of Information and Electrical Engineering, Harbin Institute of Technology at WeiHai, No. 2 WenHua west road, 264209, WeiHai, ShanDong Province, China", 
                "GuangXi Key Laboratory of Automatic Detecting Technology and Instruments (GuiLin University of Electronic Technology), GuiLin, GuangXi Province, China", 
                "State Key Laboratory of Geo-information Engineering, Xi\u2019an, ShanXi Province, China", 
                "State Key Laboratory of Satellite Navigation Engineering Technology, ShiJiaZhuang, HeBei Province, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Luo", 
            "givenName": "Qinghua", 
            "id": "sg:person.01012222774.50", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01012222774.50"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Harbin Institute of Technology", 
              "id": "https://www.grid.ac/institutes/grid.19373.3f", 
              "name": [
                "Automatic Test and Control Institute, Harbin Institute of Technology, 150080, Harbin, HeiLongJiang province, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Peng", 
            "givenName": "Yu", 
            "id": "sg:person.013371150656.83", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013371150656.83"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Harbin Institute of Technology", 
              "id": "https://www.grid.ac/institutes/grid.19373.3f", 
              "name": [
                "Automatic Test and Control Institute, Harbin Institute of Technology, 150080, Harbin, HeiLongJiang province, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Li", 
            "givenName": "Junbao", 
            "id": "sg:person.011200627256.21", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011200627256.21"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Harbin Institute of Technology", 
              "id": "https://www.grid.ac/institutes/grid.19373.3f", 
              "name": [
                "Automatic Test and Control Institute, Harbin Institute of Technology, 150080, Harbin, HeiLongJiang province, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Peng", 
            "givenName": "Xiyuan", 
            "id": "sg:person.01126451374.50", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01126451374.50"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1145/775047.775121", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004277745"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1081870.1081955", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005855339"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3390/s140406584", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018938140"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cie.2014.12.027", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020910486"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-87993-0_19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022259933", 
              "https://doi.org/10.1007/978-3-540-87993-0_19"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-87993-0_19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022259933", 
              "https://doi.org/10.1007/978-3-540-87993-0_19"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.measurement.2014.05.040", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033209796"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/pmic.200900185", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037136495"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/pmic.200900185", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037136495"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.csda.2004.12.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048487295"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2478/msr-2013-0029", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053301276"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tr.2012.2221032", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061783674"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1504/ijsnet.2014.063893", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1067492742"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icdmw.2007.88", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094289773"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/csse.2008.968", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094776180"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-09", 
        "datePublishedReg": "2017-09-01", 
        "description": "Uncertain data exist in many application fields, and there are numerous recent efforts in processing uncertain data to get more reliable results, especially uncertainty processing in clustering method. However, it is one of the urgent challenges to discover clusters with specific shape features. So we present a clustering method for data with uncertainties, and it is called multivariate wavelet principal component analysis-improved clustering using references and density (MWPCA-ICURD), which utilizes feature extraction and density-based clustering. To cluster uncertain data with specific shape original features, the original features are extracted by MWPCA method, which combines digital wavelet decomposition and principal component analysis organically. Then, a density-based clustering method ICURD is explored to discover specific shape clusters. Experimental results illustrate its validation and feasibility.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00521-016-2208-9", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.6994059", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1104357", 
            "issn": [
              "0941-0643", 
              "1433-3058"
            ], 
            "name": "Neural Computing and Applications", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "9", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "28"
          }
        ], 
        "name": "MWPCA-ICURD: density-based clustering method discovering specific shape original features", 
        "pagination": "2545-2556", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "4a1869ad87719256ebaf0ca400c6eb69b88743eff5ae1b64ea387cfa79bad326"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00521-016-2208-9"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1015551590"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00521-016-2208-9", 
          "https://app.dimensions.ai/details/publication/pub.1015551590"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T12:26", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000362_0000000362/records_87112_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs00521-016-2208-9"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00521-016-2208-9'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00521-016-2208-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00521-016-2208-9'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00521-016-2208-9'


     

    This table displays all metadata directly associated to this object as RDF triples.

    130 TRIPLES      21 PREDICATES      40 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00521-016-2208-9 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N9a815319eb3b496fbb52b6f9fc11c755
    4 schema:citation sg:pub.10.1007/978-3-540-87993-0_19
    5 https://doi.org/10.1002/pmic.200900185
    6 https://doi.org/10.1016/j.cie.2014.12.027
    7 https://doi.org/10.1016/j.csda.2004.12.010
    8 https://doi.org/10.1016/j.measurement.2014.05.040
    9 https://doi.org/10.1109/csse.2008.968
    10 https://doi.org/10.1109/icdmw.2007.88
    11 https://doi.org/10.1109/tr.2012.2221032
    12 https://doi.org/10.1145/1081870.1081955
    13 https://doi.org/10.1145/775047.775121
    14 https://doi.org/10.1504/ijsnet.2014.063893
    15 https://doi.org/10.2478/msr-2013-0029
    16 https://doi.org/10.3390/s140406584
    17 schema:datePublished 2017-09
    18 schema:datePublishedReg 2017-09-01
    19 schema:description Uncertain data exist in many application fields, and there are numerous recent efforts in processing uncertain data to get more reliable results, especially uncertainty processing in clustering method. However, it is one of the urgent challenges to discover clusters with specific shape features. So we present a clustering method for data with uncertainties, and it is called multivariate wavelet principal component analysis-improved clustering using references and density (MWPCA-ICURD), which utilizes feature extraction and density-based clustering. To cluster uncertain data with specific shape original features, the original features are extracted by MWPCA method, which combines digital wavelet decomposition and principal component analysis organically. Then, a density-based clustering method ICURD is explored to discover specific shape clusters. Experimental results illustrate its validation and feasibility.
    20 schema:genre research_article
    21 schema:inLanguage en
    22 schema:isAccessibleForFree false
    23 schema:isPartOf N6a8e4f392ad24b0896c2f54f4c9d649c
    24 N77244e50e66149a1a08eb4dfe723cf8e
    25 sg:journal.1104357
    26 schema:name MWPCA-ICURD: density-based clustering method discovering specific shape original features
    27 schema:pagination 2545-2556
    28 schema:productId N0052dc8c45b141b2b50d0fc99776a101
    29 N6feda1b03199405fa9a975e9e0849f60
    30 Nc326bef938e345c78fb2b99c576844f1
    31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015551590
    32 https://doi.org/10.1007/s00521-016-2208-9
    33 schema:sdDatePublished 2019-04-11T12:26
    34 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    35 schema:sdPublisher N7de75b1a05c7401c85e628bb18e1e996
    36 schema:url https://link.springer.com/10.1007%2Fs00521-016-2208-9
    37 sgo:license sg:explorer/license/
    38 sgo:sdDataset articles
    39 rdf:type schema:ScholarlyArticle
    40 N0052dc8c45b141b2b50d0fc99776a101 schema:name dimensions_id
    41 schema:value pub.1015551590
    42 rdf:type schema:PropertyValue
    43 N308dd3d80fbe47b6ab5babdb11a8231c rdf:first sg:person.01126451374.50
    44 rdf:rest rdf:nil
    45 N6a8e4f392ad24b0896c2f54f4c9d649c schema:issueNumber 9
    46 rdf:type schema:PublicationIssue
    47 N6feda1b03199405fa9a975e9e0849f60 schema:name readcube_id
    48 schema:value 4a1869ad87719256ebaf0ca400c6eb69b88743eff5ae1b64ea387cfa79bad326
    49 rdf:type schema:PropertyValue
    50 N77244e50e66149a1a08eb4dfe723cf8e schema:volumeNumber 28
    51 rdf:type schema:PublicationVolume
    52 N7de75b1a05c7401c85e628bb18e1e996 schema:name Springer Nature - SN SciGraph project
    53 rdf:type schema:Organization
    54 N9a815319eb3b496fbb52b6f9fc11c755 rdf:first sg:person.01012222774.50
    55 rdf:rest Ncc13cf2d6a144e7787f83ff8325a4f24
    56 Nc326bef938e345c78fb2b99c576844f1 schema:name doi
    57 schema:value 10.1007/s00521-016-2208-9
    58 rdf:type schema:PropertyValue
    59 Ncc13cf2d6a144e7787f83ff8325a4f24 rdf:first sg:person.013371150656.83
    60 rdf:rest Nd8e268cfa4824afe8acb21753ef19ded
    61 Nd8e268cfa4824afe8acb21753ef19ded rdf:first sg:person.011200627256.21
    62 rdf:rest N308dd3d80fbe47b6ab5babdb11a8231c
    63 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    64 schema:name Information and Computing Sciences
    65 rdf:type schema:DefinedTerm
    66 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    67 schema:name Artificial Intelligence and Image Processing
    68 rdf:type schema:DefinedTerm
    69 sg:grant.6994059 http://pending.schema.org/fundedItem sg:pub.10.1007/s00521-016-2208-9
    70 rdf:type schema:MonetaryGrant
    71 sg:journal.1104357 schema:issn 0941-0643
    72 1433-3058
    73 schema:name Neural Computing and Applications
    74 rdf:type schema:Periodical
    75 sg:person.01012222774.50 schema:affiliation https://www.grid.ac/institutes/grid.440723.6
    76 schema:familyName Luo
    77 schema:givenName Qinghua
    78 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01012222774.50
    79 rdf:type schema:Person
    80 sg:person.011200627256.21 schema:affiliation https://www.grid.ac/institutes/grid.19373.3f
    81 schema:familyName Li
    82 schema:givenName Junbao
    83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011200627256.21
    84 rdf:type schema:Person
    85 sg:person.01126451374.50 schema:affiliation https://www.grid.ac/institutes/grid.19373.3f
    86 schema:familyName Peng
    87 schema:givenName Xiyuan
    88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01126451374.50
    89 rdf:type schema:Person
    90 sg:person.013371150656.83 schema:affiliation https://www.grid.ac/institutes/grid.19373.3f
    91 schema:familyName Peng
    92 schema:givenName Yu
    93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013371150656.83
    94 rdf:type schema:Person
    95 sg:pub.10.1007/978-3-540-87993-0_19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022259933
    96 https://doi.org/10.1007/978-3-540-87993-0_19
    97 rdf:type schema:CreativeWork
    98 https://doi.org/10.1002/pmic.200900185 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037136495
    99 rdf:type schema:CreativeWork
    100 https://doi.org/10.1016/j.cie.2014.12.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020910486
    101 rdf:type schema:CreativeWork
    102 https://doi.org/10.1016/j.csda.2004.12.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048487295
    103 rdf:type schema:CreativeWork
    104 https://doi.org/10.1016/j.measurement.2014.05.040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033209796
    105 rdf:type schema:CreativeWork
    106 https://doi.org/10.1109/csse.2008.968 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094776180
    107 rdf:type schema:CreativeWork
    108 https://doi.org/10.1109/icdmw.2007.88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094289773
    109 rdf:type schema:CreativeWork
    110 https://doi.org/10.1109/tr.2012.2221032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061783674
    111 rdf:type schema:CreativeWork
    112 https://doi.org/10.1145/1081870.1081955 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005855339
    113 rdf:type schema:CreativeWork
    114 https://doi.org/10.1145/775047.775121 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004277745
    115 rdf:type schema:CreativeWork
    116 https://doi.org/10.1504/ijsnet.2014.063893 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067492742
    117 rdf:type schema:CreativeWork
    118 https://doi.org/10.2478/msr-2013-0029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053301276
    119 rdf:type schema:CreativeWork
    120 https://doi.org/10.3390/s140406584 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018938140
    121 rdf:type schema:CreativeWork
    122 https://www.grid.ac/institutes/grid.19373.3f schema:alternateName Harbin Institute of Technology
    123 schema:name Automatic Test and Control Institute, Harbin Institute of Technology, 150080, Harbin, HeiLongJiang province, China
    124 rdf:type schema:Organization
    125 https://www.grid.ac/institutes/grid.440723.6 schema:alternateName Guilin University of Electronic Technology
    126 schema:name GuangXi Key Laboratory of Automatic Detecting Technology and Instruments (GuiLin University of Electronic Technology), GuiLin, GuangXi Province, China
    127 School of Information and Electrical Engineering, Harbin Institute of Technology at WeiHai, No. 2 WenHua west road, 264209, WeiHai, ShanDong Province, China
    128 State Key Laboratory of Geo-information Engineering, Xi’an, ShanXi Province, China
    129 State Key Laboratory of Satellite Navigation Engineering Technology, ShiJiaZhuang, HeBei Province, China
    130 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...