A Generalized Sorting Strategy for Computer Classifications View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1966-10

AUTHORS

G. N. LANCE, W. T. WILLIAMS

ABSTRACT

AGGLOMERATIVE hierarchical methods of computer classification all begin by calculating distance-measures between elements. The hierarchy is then generated by subjecting these measures to a sorting-strategy, which depends essentially on the definition of a distance-measure between groups of elements. In nearest-neighbour sorting, this is defined as the distance between the closest pair of elements, one in each group. Macnaughton-Smith has pointed out that much more intense clustering can be produced by taking the most remote pair of elements (furthest-neighbour sorting). In group-average sorting1 the distance is defined as the mean of all between-group inter-element distances; in centroid sorting it is the distance between group centroids, defined by a conventional Euclidean model. In median2 sorting the distance of a third group from two which have just fused depends on the previous three inter-group distances in the manner of Apollonius's theorem. Although the earlier of these strategies have received some comparative assessment1,3–5 no attempt seems to have been made to generalize them into a single system. As a result, quite different computer strategies have commonly been used, necessitating a separate computer program for each. More... »

PAGES

218-218

Journal

TITLE

Nature

ISSUE

5058

VOLUME

212

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/212218a0

DOI

http://dx.doi.org/10.1038/212218a0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027187332


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/2008/0806", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information Systems", 
        "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": [
      {
        "familyName": "LANCE", 
        "givenName": "G. N.", 
        "type": "Person"
      }, 
      {
        "familyName": "WILLIAMS", 
        "givenName": "W. T.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s0065-2296(08)60249-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023661559"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/comjnl/9.1.60", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029095841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2257960", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069851279"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1966-10", 
    "datePublishedReg": "1966-10-01", 
    "description": "AGGLOMERATIVE hierarchical methods of computer classification all begin by calculating distance-measures between elements. The hierarchy is then generated by subjecting these measures to a sorting-strategy, which depends essentially on the definition of a distance-measure between groups of elements. In nearest-neighbour sorting, this is defined as the distance between the closest pair of elements, one in each group. Macnaughton-Smith has pointed out that much more intense clustering can be produced by taking the most remote pair of elements (furthest-neighbour sorting). In group-average sorting1 the distance is defined as the mean of all between-group inter-element distances; in centroid sorting it is the distance between group centroids, defined by a conventional Euclidean model. In median2 sorting the distance of a third group from two which have just fused depends on the previous three inter-group distances in the manner of Apollonius's theorem. Although the earlier of these strategies have received some comparative assessment1,3\u20135 no attempt seems to have been made to generalize them into a single system. As a result, quite different computer strategies have commonly been used, necessitating a separate computer program for each.", 
    "genre": "non_research_article", 
    "id": "sg:pub.10.1038/212218a0", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1018957", 
        "issn": [
          "0090-0028", 
          "1476-4687"
        ], 
        "name": "Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5058", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "212"
      }
    ], 
    "name": "A Generalized Sorting Strategy for Computer Classifications", 
    "pagination": "218-218", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "818253507752dcc77eae70e9c458e2890938cc50915ad7aac4f7347db108c7e5"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/212218a0"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1027187332"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/212218a0", 
      "https://app.dimensions.ai/details/publication/pub.1027187332"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:51", 
    "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/v90/0000000359_0000000359/records_29186_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/212218a0"
  }
]
 

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.10.1038/212218a0'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/212218a0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/212218a0'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/212218a0'


 

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

70 TRIPLES      21 PREDICATES      30 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/212218a0 schema:about anzsrc-for:08
2 anzsrc-for:0806
3 schema:author N6eecb048a2c44664ae17f6afe5a212b0
4 schema:citation https://doi.org/10.1016/s0065-2296(08)60249-9
5 https://doi.org/10.1093/comjnl/9.1.60
6 https://doi.org/10.2307/2257960
7 schema:datePublished 1966-10
8 schema:datePublishedReg 1966-10-01
9 schema:description AGGLOMERATIVE hierarchical methods of computer classification all begin by calculating distance-measures between elements. The hierarchy is then generated by subjecting these measures to a sorting-strategy, which depends essentially on the definition of a distance-measure between groups of elements. In nearest-neighbour sorting, this is defined as the distance between the closest pair of elements, one in each group. Macnaughton-Smith has pointed out that much more intense clustering can be produced by taking the most remote pair of elements (furthest-neighbour sorting). In group-average sorting1 the distance is defined as the mean of all between-group inter-element distances; in centroid sorting it is the distance between group centroids, defined by a conventional Euclidean model. In median2 sorting the distance of a third group from two which have just fused depends on the previous three inter-group distances in the manner of Apollonius's theorem. Although the earlier of these strategies have received some comparative assessment1,3–5 no attempt seems to have been made to generalize them into a single system. As a result, quite different computer strategies have commonly been used, necessitating a separate computer program for each.
10 schema:genre non_research_article
11 schema:inLanguage en
12 schema:isAccessibleForFree false
13 schema:isPartOf Nb2084c1b7d1f46cea75034a6b378ad06
14 Nfb0c140a2ae84e939fa2c7a077896d3b
15 sg:journal.1018957
16 schema:name A Generalized Sorting Strategy for Computer Classifications
17 schema:pagination 218-218
18 schema:productId N182678e8732541539c136a29ed65d8da
19 N1947c6d48a2a4d5380fe6d61be45557b
20 N3b30213ddf354a40a3a283fa838282ca
21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027187332
22 https://doi.org/10.1038/212218a0
23 schema:sdDatePublished 2019-04-11T11:51
24 schema:sdLicense https://scigraph.springernature.com/explorer/license/
25 schema:sdPublisher N0e6c59386726423487030bdd378e94fc
26 schema:url https://www.nature.com/articles/212218a0
27 sgo:license sg:explorer/license/
28 sgo:sdDataset articles
29 rdf:type schema:ScholarlyArticle
30 N0e6c59386726423487030bdd378e94fc schema:name Springer Nature - SN SciGraph project
31 rdf:type schema:Organization
32 N0ea6340779c1404dbd40f9c8458f82a2 rdf:first N810bb75cabe24014a1a458820c110be1
33 rdf:rest rdf:nil
34 N182678e8732541539c136a29ed65d8da schema:name readcube_id
35 schema:value 818253507752dcc77eae70e9c458e2890938cc50915ad7aac4f7347db108c7e5
36 rdf:type schema:PropertyValue
37 N1947c6d48a2a4d5380fe6d61be45557b schema:name dimensions_id
38 schema:value pub.1027187332
39 rdf:type schema:PropertyValue
40 N34a32a78aaf94299b15b674c21933cbb schema:familyName LANCE
41 schema:givenName G. N.
42 rdf:type schema:Person
43 N3b30213ddf354a40a3a283fa838282ca schema:name doi
44 schema:value 10.1038/212218a0
45 rdf:type schema:PropertyValue
46 N6eecb048a2c44664ae17f6afe5a212b0 rdf:first N34a32a78aaf94299b15b674c21933cbb
47 rdf:rest N0ea6340779c1404dbd40f9c8458f82a2
48 N810bb75cabe24014a1a458820c110be1 schema:familyName WILLIAMS
49 schema:givenName W. T.
50 rdf:type schema:Person
51 Nb2084c1b7d1f46cea75034a6b378ad06 schema:volumeNumber 212
52 rdf:type schema:PublicationVolume
53 Nfb0c140a2ae84e939fa2c7a077896d3b schema:issueNumber 5058
54 rdf:type schema:PublicationIssue
55 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
56 schema:name Information and Computing Sciences
57 rdf:type schema:DefinedTerm
58 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
59 schema:name Information Systems
60 rdf:type schema:DefinedTerm
61 sg:journal.1018957 schema:issn 0090-0028
62 1476-4687
63 schema:name Nature
64 rdf:type schema:Periodical
65 https://doi.org/10.1016/s0065-2296(08)60249-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023661559
66 rdf:type schema:CreativeWork
67 https://doi.org/10.1093/comjnl/9.1.60 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029095841
68 rdf:type schema:CreativeWork
69 https://doi.org/10.2307/2257960 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069851279
70 rdf:type schema:CreativeWork
 




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


...