An algorithm for generating artificial test clusters View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1985-03

AUTHORS

Glenn W. Milligan

ABSTRACT

An algorithm for generating artificial data sets which contain distinct nonoverlapping clusters is presented. The algorithm is useful for generating test data sets for Monte Carlo validation research conducted on clustering methods or statistics. The algorithm generates data sets which contain either 1, 2, 3, 4, or 5 clusters. By default, the data are embedded in either a 4, 6, or 8 dimensional space. Three different patterns for assigning the points to the clusters are provided. One pattern assigns the points equally to the clusters while the remaining two schemes produce clusters of unequal sizes. Finally, a number of methods for introducing error in the data have been incorporated in the algorithm. More... »

PAGES

123-127

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02294153

DOI

http://dx.doi.org/10.1007/bf02294153

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040760134


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": "The Ohio State University", 
          "id": "https://www.grid.ac/institutes/grid.261331.4", 
          "name": [
            "Faculty of Management Sciences, The Ohio State University, 301 Hagerty Hall, 43210, Columbus, OH"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Milligan", 
        "givenName": "Glenn W.", 
        "id": "sg:person.0724105645.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0724105645.42"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1207/s15327906mbr1403_6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007334311"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02293899", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008979876", 
          "https://doi.org/10.1007/bf02293899"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0013164484441003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022819826"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0013164484441003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022819826"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1037/0033-2909.83.3.377", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023264680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-3203(80)90002-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023816311"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-3203(80)90002-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023816311"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1540-5915.1980.tb01168.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025064123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1207/s15327906mbr2001_6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030308299"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1207/s15327906mbr1603_7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030855950"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-3203(79)90034-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034901103"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-3203(79)90034-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034901103"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/comjnl/20.4.359", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036668586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02293907", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037645608", 
          "https://doi.org/10.1007/bf02293907"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-3203(80)90001-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046710985"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-3203(80)90001-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046710985"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/014662168000400107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053831006"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/014662168000400107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053831006"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.1983.4767342", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061741925"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1985-03", 
    "datePublishedReg": "1985-03-01", 
    "description": "An algorithm for generating artificial data sets which contain distinct nonoverlapping clusters is presented. The algorithm is useful for generating test data sets for Monte Carlo validation research conducted on clustering methods or statistics. The algorithm generates data sets which contain either 1, 2, 3, 4, or 5 clusters. By default, the data are embedded in either a 4, 6, or 8 dimensional space. Three different patterns for assigning the points to the clusters are provided. One pattern assigns the points equally to the clusters while the remaining two schemes produce clusters of unequal sizes. Finally, a number of methods for introducing error in the data have been incorporated in the algorithm.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf02294153", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1017907", 
        "issn": [
          "0033-3123", 
          "1860-0980"
        ], 
        "name": "Psychometrika", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "50"
      }
    ], 
    "name": "An algorithm for generating artificial test clusters", 
    "pagination": "123-127", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "c22a53e02f18c03aeccc8b60b55090428b4368511127b2daf05d071b83b21b36"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf02294153"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1040760134"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf02294153", 
      "https://app.dimensions.ai/details/publication/pub.1040760134"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:41", 
    "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/v90/0000000363_0000000363/records_70053_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF02294153"
  }
]
 

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/bf02294153'

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/bf02294153'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf02294153'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf02294153'


 

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

105 TRIPLES      21 PREDICATES      41 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf02294153 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nba07e85927044e528eb0574b26ada4cd
4 schema:citation sg:pub.10.1007/bf02293899
5 sg:pub.10.1007/bf02293907
6 https://doi.org/10.1016/0031-3203(79)90034-7
7 https://doi.org/10.1016/0031-3203(80)90001-1
8 https://doi.org/10.1016/0031-3203(80)90002-3
9 https://doi.org/10.1037/0033-2909.83.3.377
10 https://doi.org/10.1093/comjnl/20.4.359
11 https://doi.org/10.1109/tpami.1983.4767342
12 https://doi.org/10.1111/j.1540-5915.1980.tb01168.x
13 https://doi.org/10.1177/0013164484441003
14 https://doi.org/10.1177/014662168000400107
15 https://doi.org/10.1207/s15327906mbr1403_6
16 https://doi.org/10.1207/s15327906mbr1603_7
17 https://doi.org/10.1207/s15327906mbr2001_6
18 schema:datePublished 1985-03
19 schema:datePublishedReg 1985-03-01
20 schema:description An algorithm for generating artificial data sets which contain distinct nonoverlapping clusters is presented. The algorithm is useful for generating test data sets for Monte Carlo validation research conducted on clustering methods or statistics. The algorithm generates data sets which contain either 1, 2, 3, 4, or 5 clusters. By default, the data are embedded in either a 4, 6, or 8 dimensional space. Three different patterns for assigning the points to the clusters are provided. One pattern assigns the points equally to the clusters while the remaining two schemes produce clusters of unequal sizes. Finally, a number of methods for introducing error in the data have been incorporated in the algorithm.
21 schema:genre research_article
22 schema:inLanguage en
23 schema:isAccessibleForFree false
24 schema:isPartOf N14bcda7adc844a378647d66ec16581e9
25 N831c6b3c581f41da9cc55a2d57029b19
26 sg:journal.1017907
27 schema:name An algorithm for generating artificial test clusters
28 schema:pagination 123-127
29 schema:productId N719898065673424a9de845b76bc4f106
30 N96b10fcfeede407b8acffab350d5e1ec
31 Nd8af00e0c8a645e6970945730f293906
32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040760134
33 https://doi.org/10.1007/bf02294153
34 schema:sdDatePublished 2019-04-11T12:41
35 schema:sdLicense https://scigraph.springernature.com/explorer/license/
36 schema:sdPublisher N2bbeb5d055134796b93236c9022aa15c
37 schema:url http://link.springer.com/10.1007/BF02294153
38 sgo:license sg:explorer/license/
39 sgo:sdDataset articles
40 rdf:type schema:ScholarlyArticle
41 N14bcda7adc844a378647d66ec16581e9 schema:issueNumber 1
42 rdf:type schema:PublicationIssue
43 N2bbeb5d055134796b93236c9022aa15c schema:name Springer Nature - SN SciGraph project
44 rdf:type schema:Organization
45 N719898065673424a9de845b76bc4f106 schema:name readcube_id
46 schema:value c22a53e02f18c03aeccc8b60b55090428b4368511127b2daf05d071b83b21b36
47 rdf:type schema:PropertyValue
48 N831c6b3c581f41da9cc55a2d57029b19 schema:volumeNumber 50
49 rdf:type schema:PublicationVolume
50 N96b10fcfeede407b8acffab350d5e1ec schema:name dimensions_id
51 schema:value pub.1040760134
52 rdf:type schema:PropertyValue
53 Nba07e85927044e528eb0574b26ada4cd rdf:first sg:person.0724105645.42
54 rdf:rest rdf:nil
55 Nd8af00e0c8a645e6970945730f293906 schema:name doi
56 schema:value 10.1007/bf02294153
57 rdf:type schema:PropertyValue
58 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
59 schema:name Information and Computing Sciences
60 rdf:type schema:DefinedTerm
61 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
62 schema:name Artificial Intelligence and Image Processing
63 rdf:type schema:DefinedTerm
64 sg:journal.1017907 schema:issn 0033-3123
65 1860-0980
66 schema:name Psychometrika
67 rdf:type schema:Periodical
68 sg:person.0724105645.42 schema:affiliation https://www.grid.ac/institutes/grid.261331.4
69 schema:familyName Milligan
70 schema:givenName Glenn W.
71 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0724105645.42
72 rdf:type schema:Person
73 sg:pub.10.1007/bf02293899 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008979876
74 https://doi.org/10.1007/bf02293899
75 rdf:type schema:CreativeWork
76 sg:pub.10.1007/bf02293907 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037645608
77 https://doi.org/10.1007/bf02293907
78 rdf:type schema:CreativeWork
79 https://doi.org/10.1016/0031-3203(79)90034-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034901103
80 rdf:type schema:CreativeWork
81 https://doi.org/10.1016/0031-3203(80)90001-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046710985
82 rdf:type schema:CreativeWork
83 https://doi.org/10.1016/0031-3203(80)90002-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023816311
84 rdf:type schema:CreativeWork
85 https://doi.org/10.1037/0033-2909.83.3.377 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023264680
86 rdf:type schema:CreativeWork
87 https://doi.org/10.1093/comjnl/20.4.359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036668586
88 rdf:type schema:CreativeWork
89 https://doi.org/10.1109/tpami.1983.4767342 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061741925
90 rdf:type schema:CreativeWork
91 https://doi.org/10.1111/j.1540-5915.1980.tb01168.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1025064123
92 rdf:type schema:CreativeWork
93 https://doi.org/10.1177/0013164484441003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022819826
94 rdf:type schema:CreativeWork
95 https://doi.org/10.1177/014662168000400107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053831006
96 rdf:type schema:CreativeWork
97 https://doi.org/10.1207/s15327906mbr1403_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007334311
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1207/s15327906mbr1603_7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030855950
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1207/s15327906mbr2001_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030308299
102 rdf:type schema:CreativeWork
103 https://www.grid.ac/institutes/grid.261331.4 schema:alternateName The Ohio State University
104 schema:name Faculty of Management Sciences, The Ohio State University, 301 Hagerty Hall, 43210, Columbus, OH
105 rdf:type schema:Organization
 




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


...