Optimization and improvement of data mining algorithm based on efficient incremental kernel fuzzy clustering for large data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-03-05

AUTHORS

Cuifen Zhang, Lina Hao, Li Fan

ABSTRACT

The arrival of the big data era in the new century has made the traditional data mining algorithms unable to meet the requirements of big data mining in accuracy and efficiency. Therefore, a data mining algorithm based on efficient incremental kernel fuzzy clustering for big data was optimized—in this paper. First of all, the methods of big data mining and fuzzy clustering technique for data mining were summarized. Then, the data mining algorithm based on the incremental kernel fuzzy clustering was optimized. Finally, the method was validated by comparing with the stKFCM algorithm. The verification results showed that the improved algorithm was superior in performance and accuracy, but only a slight gap in running time. More... »

PAGES

1-10

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10586-018-1767-1

DOI

http://dx.doi.org/10.1007/s10586-018-1767-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101353724


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": "Shandong Women\u2019s University", 
          "id": "https://www.grid.ac/institutes/grid.495262.e", 
          "name": [
            "School of Information Technology, Shandong Women\u2019s University, 250300, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Cuifen", 
        "id": "sg:person.016144277440.95", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016144277440.95"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Chengdu University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.411288.6", 
          "name": [
            "College of Earth Sciences, Chengdu University of Technology (CDUT), 610059, Chengdu, China", 
            "Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources of the People\u2019s Republic of China, 610059, Chengdu, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hao", 
        "givenName": "Lina", 
        "id": "sg:person.07441226240.68", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07441226240.68"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong Jianzhu University", 
          "id": "https://www.grid.ac/institutes/grid.440623.7", 
          "name": [
            "Shandong Jianzhu University, 250101, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fan", 
        "givenName": "Li", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1023/b:clus.0000004028.17653.cb", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001300008", 
          "https://doi.org/10.1023/b:clus.0000004028.17653.cb"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10586-012-0213-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015812784", 
          "https://doi.org/10.1007/s10586-012-0213-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cardfail.2014.03.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016504555"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejps.2015.03.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017977407"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2478/eurodl-2014-0008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018208900"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10586-007-0043-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023455310", 
          "https://doi.org/10.1007/s10586-007-0043-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10586-007-0043-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023455310", 
          "https://doi.org/10.1007/s10586-007-0043-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10586-008-0070-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026471919", 
          "https://doi.org/10.1007/s10586-008-0070-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10586-008-0070-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026471919", 
          "https://doi.org/10.1007/s10586-008-0070-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-15-s6-i1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027384637", 
          "https://doi.org/10.1186/1471-2105-15-s6-i1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10994-013-5334-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029734350", 
          "https://doi.org/10.1007/s10994-013-5334-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10994-013-5334-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029734350", 
          "https://doi.org/10.1007/s10994-013-5334-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/thoraxjnl-2013-203601", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031837260"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/thoraxjnl-2013-203601", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031837260"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00778-011-0262-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038182159", 
          "https://doi.org/10.1007/s00778-011-0262-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bib/bbs034", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039919127"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2014.04.024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041088127"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tevc.2013.2290086", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061605190"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tkde.2012.71", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061662660"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2013.107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061744432"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13762-017-1351-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085409538", 
          "https://doi.org/10.1007/s13762-017-1351-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13762-017-1351-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085409538", 
          "https://doi.org/10.1007/s13762-017-1351-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10586-017-0982-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086124025", 
          "https://doi.org/10.1007/s10586-017-0982-5"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-03-05", 
    "datePublishedReg": "2018-03-05", 
    "description": "The arrival of the big data era in the new century has made the traditional data mining algorithms unable to meet the requirements of big data mining in accuracy and efficiency. Therefore, a data mining algorithm based on efficient incremental kernel fuzzy clustering for big data was optimized\u2014in this paper. First of all, the methods of big data mining and fuzzy clustering technique for data mining were summarized. Then, the data mining algorithm based on the incremental kernel fuzzy clustering was optimized. Finally, the method was validated by comparing with the stKFCM algorithm. The verification results showed that the improved algorithm was superior in performance and accuracy, but only a slight gap in running time.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10586-018-1767-1", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1046649", 
        "issn": [
          "1386-7857", 
          "1573-7543"
        ], 
        "name": "Cluster Computing", 
        "type": "Periodical"
      }
    ], 
    "name": "Optimization and improvement of data mining algorithm based on efficient incremental kernel fuzzy clustering for large data", 
    "pagination": "1-10", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "eff333cc470ef92e4d7af323121d453cf0d64cfe6e97a1ddd31c29dc8ce2e923"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10586-018-1767-1"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1101353724"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10586-018-1767-1", 
      "https://app.dimensions.ai/details/publication/pub.1101353724"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:59", 
    "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/v90/0000000352_0000000352/records_60335_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10586-018-1767-1"
  }
]
 

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/s10586-018-1767-1'

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/s10586-018-1767-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10586-018-1767-1'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10586-018-1767-1'


 

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

138 TRIPLES      21 PREDICATES      42 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10586-018-1767-1 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N48108205fadc404fa9bed05a9f5a175e
4 schema:citation sg:pub.10.1007/s00778-011-0262-6
5 sg:pub.10.1007/s10586-007-0043-6
6 sg:pub.10.1007/s10586-008-0070-y
7 sg:pub.10.1007/s10586-012-0213-z
8 sg:pub.10.1007/s10586-017-0982-5
9 sg:pub.10.1007/s10994-013-5334-y
10 sg:pub.10.1007/s13762-017-1351-x
11 sg:pub.10.1023/b:clus.0000004028.17653.cb
12 sg:pub.10.1186/1471-2105-15-s6-i1
13 https://doi.org/10.1016/j.cardfail.2014.03.008
14 https://doi.org/10.1016/j.ejps.2015.03.013
15 https://doi.org/10.1016/j.eswa.2014.04.024
16 https://doi.org/10.1093/bib/bbs034
17 https://doi.org/10.1109/tevc.2013.2290086
18 https://doi.org/10.1109/tkde.2012.71
19 https://doi.org/10.1109/tpami.2013.107
20 https://doi.org/10.1136/thoraxjnl-2013-203601
21 https://doi.org/10.2478/eurodl-2014-0008
22 schema:datePublished 2018-03-05
23 schema:datePublishedReg 2018-03-05
24 schema:description The arrival of the big data era in the new century has made the traditional data mining algorithms unable to meet the requirements of big data mining in accuracy and efficiency. Therefore, a data mining algorithm based on efficient incremental kernel fuzzy clustering for big data was optimized—in this paper. First of all, the methods of big data mining and fuzzy clustering technique for data mining were summarized. Then, the data mining algorithm based on the incremental kernel fuzzy clustering was optimized. Finally, the method was validated by comparing with the stKFCM algorithm. The verification results showed that the improved algorithm was superior in performance and accuracy, but only a slight gap in running time.
25 schema:genre research_article
26 schema:inLanguage en
27 schema:isAccessibleForFree false
28 schema:isPartOf sg:journal.1046649
29 schema:name Optimization and improvement of data mining algorithm based on efficient incremental kernel fuzzy clustering for large data
30 schema:pagination 1-10
31 schema:productId N58bf945530de49e1a9eca97753f3e3cb
32 N5b8b60778e894754b0305930fdbeb777
33 Nd7447216313a4a14b6ba72d1532082ef
34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101353724
35 https://doi.org/10.1007/s10586-018-1767-1
36 schema:sdDatePublished 2019-04-11T10:59
37 schema:sdLicense https://scigraph.springernature.com/explorer/license/
38 schema:sdPublisher Nc72080a0bb2542b7922211a0dafcb44e
39 schema:url https://link.springer.com/10.1007%2Fs10586-018-1767-1
40 sgo:license sg:explorer/license/
41 sgo:sdDataset articles
42 rdf:type schema:ScholarlyArticle
43 N370e786e4f9547e7ada8fe16c4ba4d23 rdf:first Nb76a04fd0c43441f9cc33e05189da2b9
44 rdf:rest rdf:nil
45 N40a2784f975549e38f56268d223b32a4 rdf:first sg:person.07441226240.68
46 rdf:rest N370e786e4f9547e7ada8fe16c4ba4d23
47 N48108205fadc404fa9bed05a9f5a175e rdf:first sg:person.016144277440.95
48 rdf:rest N40a2784f975549e38f56268d223b32a4
49 N58bf945530de49e1a9eca97753f3e3cb schema:name doi
50 schema:value 10.1007/s10586-018-1767-1
51 rdf:type schema:PropertyValue
52 N5b8b60778e894754b0305930fdbeb777 schema:name readcube_id
53 schema:value eff333cc470ef92e4d7af323121d453cf0d64cfe6e97a1ddd31c29dc8ce2e923
54 rdf:type schema:PropertyValue
55 Nb76a04fd0c43441f9cc33e05189da2b9 schema:affiliation https://www.grid.ac/institutes/grid.440623.7
56 schema:familyName Fan
57 schema:givenName Li
58 rdf:type schema:Person
59 Nc72080a0bb2542b7922211a0dafcb44e schema:name Springer Nature - SN SciGraph project
60 rdf:type schema:Organization
61 Nd7447216313a4a14b6ba72d1532082ef schema:name dimensions_id
62 schema:value pub.1101353724
63 rdf:type schema:PropertyValue
64 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
65 schema:name Information and Computing Sciences
66 rdf:type schema:DefinedTerm
67 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
68 schema:name Artificial Intelligence and Image Processing
69 rdf:type schema:DefinedTerm
70 sg:journal.1046649 schema:issn 1386-7857
71 1573-7543
72 schema:name Cluster Computing
73 rdf:type schema:Periodical
74 sg:person.016144277440.95 schema:affiliation https://www.grid.ac/institutes/grid.495262.e
75 schema:familyName Zhang
76 schema:givenName Cuifen
77 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016144277440.95
78 rdf:type schema:Person
79 sg:person.07441226240.68 schema:affiliation https://www.grid.ac/institutes/grid.411288.6
80 schema:familyName Hao
81 schema:givenName Lina
82 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07441226240.68
83 rdf:type schema:Person
84 sg:pub.10.1007/s00778-011-0262-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038182159
85 https://doi.org/10.1007/s00778-011-0262-6
86 rdf:type schema:CreativeWork
87 sg:pub.10.1007/s10586-007-0043-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023455310
88 https://doi.org/10.1007/s10586-007-0043-6
89 rdf:type schema:CreativeWork
90 sg:pub.10.1007/s10586-008-0070-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1026471919
91 https://doi.org/10.1007/s10586-008-0070-y
92 rdf:type schema:CreativeWork
93 sg:pub.10.1007/s10586-012-0213-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1015812784
94 https://doi.org/10.1007/s10586-012-0213-z
95 rdf:type schema:CreativeWork
96 sg:pub.10.1007/s10586-017-0982-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086124025
97 https://doi.org/10.1007/s10586-017-0982-5
98 rdf:type schema:CreativeWork
99 sg:pub.10.1007/s10994-013-5334-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1029734350
100 https://doi.org/10.1007/s10994-013-5334-y
101 rdf:type schema:CreativeWork
102 sg:pub.10.1007/s13762-017-1351-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1085409538
103 https://doi.org/10.1007/s13762-017-1351-x
104 rdf:type schema:CreativeWork
105 sg:pub.10.1023/b:clus.0000004028.17653.cb schema:sameAs https://app.dimensions.ai/details/publication/pub.1001300008
106 https://doi.org/10.1023/b:clus.0000004028.17653.cb
107 rdf:type schema:CreativeWork
108 sg:pub.10.1186/1471-2105-15-s6-i1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027384637
109 https://doi.org/10.1186/1471-2105-15-s6-i1
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/j.cardfail.2014.03.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016504555
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1016/j.ejps.2015.03.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017977407
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1016/j.eswa.2014.04.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041088127
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1093/bib/bbs034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039919127
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1109/tevc.2013.2290086 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061605190
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1109/tkde.2012.71 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061662660
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1109/tpami.2013.107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061744432
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1136/thoraxjnl-2013-203601 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031837260
126 rdf:type schema:CreativeWork
127 https://doi.org/10.2478/eurodl-2014-0008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018208900
128 rdf:type schema:CreativeWork
129 https://www.grid.ac/institutes/grid.411288.6 schema:alternateName Chengdu University of Technology
130 schema:name College of Earth Sciences, Chengdu University of Technology (CDUT), 610059, Chengdu, China
131 Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources of the People’s Republic of China, 610059, Chengdu, China
132 rdf:type schema:Organization
133 https://www.grid.ac/institutes/grid.440623.7 schema:alternateName Shandong Jianzhu University
134 schema:name Shandong Jianzhu University, 250101, Jinan, China
135 rdf:type schema:Organization
136 https://www.grid.ac/institutes/grid.495262.e schema:alternateName Shandong Women’s University
137 schema:name School of Information Technology, Shandong Women’s University, 250300, Jinan, China
138 rdf:type schema:Organization
 




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


...