Detecting and ranking outliers in high-dimensional data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12-14

AUTHORS

Amardeep Kaur, Amitava Datta

ABSTRACT

Detecting outliers in high-dimensional data is a challenging problem. In high-dimensional data, outlying behaviour of data points can only be detected in the locally relevant subsets of data dimensions. The subsets of dimensions are called subspaces and the number of these subspaces grows exponentially with increase in data dimensionality. A data point which is an outlier in one subspace can appear normal in another subspace. In order to characterise an outlier, it is important to measure its outlying behaviour according to the number of subspaces in which it shows up as an outlier. These additional details can aid a data analyst to make important decisions about what to do with an outlier in terms of removing, fixing or keeping it unchanged in the dataset. In this paper, we propose an effective outlier detection algorithm for high-dimensional data which is based on a recent density-based clustering algorithm called SUBSCALE. We also provide ranking of outliers in terms of strength of their outlying behaviour. Our outlier detection and ranking algorithm does not make any assumptions about the underlying data distribution and can adapt according to different density parameter settings. We experimented with different datasets, and the top-ranked outliers were predicted with more than 82% precision as well as recall. More... »

PAGES

1-13

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12572-018-0240-y

DOI

http://dx.doi.org/10.1007/s12572-018-0240-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110639014


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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Western Australia", 
          "id": "https://www.grid.ac/institutes/grid.1012.2", 
          "name": [
            "School of Computer Science and Software Engineering, University of Western Australia, Perth, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kaur", 
        "givenName": "Amardeep", 
        "id": "sg:person.011201737233.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011201737233.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Western Australia", 
          "id": "https://www.grid.ac/institutes/grid.1012.2", 
          "name": [
            "School of Computer Science and Software Engineering, University of Western Australia, Perth, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Datta", 
        "givenName": "Amitava", 
        "id": "sg:person.014200235740.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014200235740.41"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1145/375663.375668", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004457232"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1541880.1541883", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004686590"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1475-925x-6-23", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004948100", 
          "https://doi.org/10.1186/1475-925x-6-23"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csda.2007.05.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005436711"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10115-006-0020-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006754616", 
          "https://doi.org/10.1007/s10115-006-0020-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10115-006-0020-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006754616", 
          "https://doi.org/10.1007/s10115-006-0020-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1401890.1401946", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008072501"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0002-9343(97)00244-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009214403"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/amiajnl-2011-000681", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009487579"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/b:aire.0000045502.10941.a9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014095928", 
          "https://doi.org/10.1023/b:aire.0000045502.10941.a9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nsr/nwt032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014829674"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/b978-012088469-8.50123-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020508151"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10618-008-0093-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020736875", 
          "https://doi.org/10.1007/s10618-008-0093-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s40537-015-0027-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023441756", 
          "https://doi.org/10.1186/s40537-015-0027-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s40537-015-0027-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023441756", 
          "https://doi.org/10.1186/s40537-015-0027-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/07421222.1996.11518099", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030081599"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4018/jdm.2005010102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030524071"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1541880.1541882", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030762489"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pmed.0020267", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033836150"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pmed.0020267", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033836150"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0167-9473(96)00027-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034778667"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/276304.276314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034779247"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-01307-2_86", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034820545", 
          "https://doi.org/10.1007/978-3-642-01307-2_86"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-01307-2_86", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034820545", 
          "https://doi.org/10.1007/978-3-642-01307-2_86"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/269012.269025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037814739"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1021564703268", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040346089", 
          "https://doi.org/10.1023/a:1021564703268"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4614-6396-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040683513", 
          "https://doi.org/10.1007/978-1-4614-6396-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4614-6396-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040683513", 
          "https://doi.org/10.1007/978-1-4614-6396-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sam.11161", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041615658"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1515/9781400874668", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043152849"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-015-3994-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053674345", 
          "https://doi.org/10.1007/978-94-015-3994-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-015-3994-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053674345", 
          "https://doi.org/10.1007/978-94-015-3994-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/2.970578", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061106475"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmj.310.6987.1122", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062771749"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmj.310.6987.1122", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062771749"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/276305.276314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063165004"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/335191.335437", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063168484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3926/jiem.2011.v4n2.p168-193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071706223"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1077047662", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-67162-8_21", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091560246", 
          "https://doi.org/10.1007/978-3-319-67162-8_21"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icde.2011.5767852", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093382456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icde.2012.88", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093611499"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icde.2011.5767916", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095048963"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0471448354", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098661481"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0471448354", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098661481"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12-14", 
    "datePublishedReg": "2018-12-14", 
    "description": "Detecting outliers in high-dimensional data is a challenging problem. In high-dimensional data, outlying behaviour of data points can only be detected in the locally relevant subsets of data dimensions. The subsets of dimensions are called subspaces and the number of these subspaces grows exponentially with increase in data dimensionality. A data point which is an outlier in one subspace can appear normal in another subspace. In order to characterise an outlier, it is important to measure its outlying behaviour according to the number of subspaces in which it shows up as an outlier. These additional details can aid a data analyst to make important decisions about what to do with an outlier in terms of removing, fixing or keeping it unchanged in the dataset. In this paper, we propose an effective outlier detection algorithm for high-dimensional data which is based on a recent density-based clustering algorithm called SUBSCALE. We also provide ranking of outliers in terms of strength of their outlying behaviour. Our outlier detection and ranking algorithm does not make any assumptions about the underlying data distribution and can adapt according to different density parameter settings. We experimented with different datasets, and the top-ranked outliers were predicted with more than 82% precision as well as recall.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s12572-018-0240-y", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1050051", 
        "issn": [
          "0975-0770", 
          "0975-5616"
        ], 
        "name": "International Journal of Advances in Engineering Sciences and Applied Mathematics", 
        "type": "Periodical"
      }
    ], 
    "name": "Detecting and ranking outliers in high-dimensional data", 
    "pagination": "1-13", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "2409f22ad5ce632dbf982d28947463d38d2d955a494685e2ed681d3642810da9"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12572-018-0240-y"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1110639014"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12572-018-0240-y", 
      "https://app.dimensions.ai/details/publication/pub.1110639014"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T08:24", 
    "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/v90/0000000296_0000000296/records_57219_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs12572-018-0240-y"
  }
]
 

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/s12572-018-0240-y'

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/s12572-018-0240-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12572-018-0240-y'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12572-018-0240-y'


 

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

182 TRIPLES      21 PREDICATES      61 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12572-018-0240-y schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Ncf6f6e396eba4fd7b1b9d61de989fdea
4 schema:citation sg:pub.10.1007/978-1-4614-6396-2
5 sg:pub.10.1007/978-3-319-67162-8_21
6 sg:pub.10.1007/978-3-642-01307-2_86
7 sg:pub.10.1007/978-94-015-3994-4
8 sg:pub.10.1007/s10115-006-0020-z
9 sg:pub.10.1007/s10618-008-0093-2
10 sg:pub.10.1023/a:1021564703268
11 sg:pub.10.1023/b:aire.0000045502.10941.a9
12 sg:pub.10.1186/1475-925x-6-23
13 sg:pub.10.1186/s40537-015-0027-y
14 https://app.dimensions.ai/details/publication/pub.1077047662
15 https://doi.org/10.1002/0471448354
16 https://doi.org/10.1002/sam.11161
17 https://doi.org/10.1016/b978-012088469-8.50123-6
18 https://doi.org/10.1016/j.csda.2007.05.018
19 https://doi.org/10.1016/s0002-9343(97)00244-1
20 https://doi.org/10.1016/s0167-9473(96)00027-8
21 https://doi.org/10.1080/07421222.1996.11518099
22 https://doi.org/10.1093/nsr/nwt032
23 https://doi.org/10.1109/2.970578
24 https://doi.org/10.1109/icde.2011.5767852
25 https://doi.org/10.1109/icde.2011.5767916
26 https://doi.org/10.1109/icde.2012.88
27 https://doi.org/10.1136/amiajnl-2011-000681
28 https://doi.org/10.1136/bmj.310.6987.1122
29 https://doi.org/10.1145/1401890.1401946
30 https://doi.org/10.1145/1541880.1541882
31 https://doi.org/10.1145/1541880.1541883
32 https://doi.org/10.1145/269012.269025
33 https://doi.org/10.1145/276304.276314
34 https://doi.org/10.1145/276305.276314
35 https://doi.org/10.1145/335191.335437
36 https://doi.org/10.1145/375663.375668
37 https://doi.org/10.1371/journal.pmed.0020267
38 https://doi.org/10.1515/9781400874668
39 https://doi.org/10.3926/jiem.2011.v4n2.p168-193
40 https://doi.org/10.4018/jdm.2005010102
41 schema:datePublished 2018-12-14
42 schema:datePublishedReg 2018-12-14
43 schema:description Detecting outliers in high-dimensional data is a challenging problem. In high-dimensional data, outlying behaviour of data points can only be detected in the locally relevant subsets of data dimensions. The subsets of dimensions are called subspaces and the number of these subspaces grows exponentially with increase in data dimensionality. A data point which is an outlier in one subspace can appear normal in another subspace. In order to characterise an outlier, it is important to measure its outlying behaviour according to the number of subspaces in which it shows up as an outlier. These additional details can aid a data analyst to make important decisions about what to do with an outlier in terms of removing, fixing or keeping it unchanged in the dataset. In this paper, we propose an effective outlier detection algorithm for high-dimensional data which is based on a recent density-based clustering algorithm called SUBSCALE. We also provide ranking of outliers in terms of strength of their outlying behaviour. Our outlier detection and ranking algorithm does not make any assumptions about the underlying data distribution and can adapt according to different density parameter settings. We experimented with different datasets, and the top-ranked outliers were predicted with more than 82% precision as well as recall.
44 schema:genre research_article
45 schema:inLanguage en
46 schema:isAccessibleForFree false
47 schema:isPartOf sg:journal.1050051
48 schema:name Detecting and ranking outliers in high-dimensional data
49 schema:pagination 1-13
50 schema:productId N220919e82ba54016be900cbc9e9b621f
51 N4fbe05a1e9ec4647b2617cc57cbc5444
52 N74df5ed5a2a44858b38474d20dd0df04
53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110639014
54 https://doi.org/10.1007/s12572-018-0240-y
55 schema:sdDatePublished 2019-04-11T08:24
56 schema:sdLicense https://scigraph.springernature.com/explorer/license/
57 schema:sdPublisher N5261605f6ec34a10a79ebb2ee9e71be4
58 schema:url https://link.springer.com/10.1007%2Fs12572-018-0240-y
59 sgo:license sg:explorer/license/
60 sgo:sdDataset articles
61 rdf:type schema:ScholarlyArticle
62 N220919e82ba54016be900cbc9e9b621f schema:name dimensions_id
63 schema:value pub.1110639014
64 rdf:type schema:PropertyValue
65 N4fbe05a1e9ec4647b2617cc57cbc5444 schema:name doi
66 schema:value 10.1007/s12572-018-0240-y
67 rdf:type schema:PropertyValue
68 N5261605f6ec34a10a79ebb2ee9e71be4 schema:name Springer Nature - SN SciGraph project
69 rdf:type schema:Organization
70 N74df5ed5a2a44858b38474d20dd0df04 schema:name readcube_id
71 schema:value 2409f22ad5ce632dbf982d28947463d38d2d955a494685e2ed681d3642810da9
72 rdf:type schema:PropertyValue
73 Ncf6f6e396eba4fd7b1b9d61de989fdea rdf:first sg:person.011201737233.09
74 rdf:rest Ne642e4ac5db44910b2ba1449123c8866
75 Ne642e4ac5db44910b2ba1449123c8866 rdf:first sg:person.014200235740.41
76 rdf:rest rdf:nil
77 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
78 schema:name Mathematical Sciences
79 rdf:type schema:DefinedTerm
80 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
81 schema:name Statistics
82 rdf:type schema:DefinedTerm
83 sg:journal.1050051 schema:issn 0975-0770
84 0975-5616
85 schema:name International Journal of Advances in Engineering Sciences and Applied Mathematics
86 rdf:type schema:Periodical
87 sg:person.011201737233.09 schema:affiliation https://www.grid.ac/institutes/grid.1012.2
88 schema:familyName Kaur
89 schema:givenName Amardeep
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011201737233.09
91 rdf:type schema:Person
92 sg:person.014200235740.41 schema:affiliation https://www.grid.ac/institutes/grid.1012.2
93 schema:familyName Datta
94 schema:givenName Amitava
95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014200235740.41
96 rdf:type schema:Person
97 sg:pub.10.1007/978-1-4614-6396-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040683513
98 https://doi.org/10.1007/978-1-4614-6396-2
99 rdf:type schema:CreativeWork
100 sg:pub.10.1007/978-3-319-67162-8_21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091560246
101 https://doi.org/10.1007/978-3-319-67162-8_21
102 rdf:type schema:CreativeWork
103 sg:pub.10.1007/978-3-642-01307-2_86 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034820545
104 https://doi.org/10.1007/978-3-642-01307-2_86
105 rdf:type schema:CreativeWork
106 sg:pub.10.1007/978-94-015-3994-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053674345
107 https://doi.org/10.1007/978-94-015-3994-4
108 rdf:type schema:CreativeWork
109 sg:pub.10.1007/s10115-006-0020-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1006754616
110 https://doi.org/10.1007/s10115-006-0020-z
111 rdf:type schema:CreativeWork
112 sg:pub.10.1007/s10618-008-0093-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020736875
113 https://doi.org/10.1007/s10618-008-0093-2
114 rdf:type schema:CreativeWork
115 sg:pub.10.1023/a:1021564703268 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040346089
116 https://doi.org/10.1023/a:1021564703268
117 rdf:type schema:CreativeWork
118 sg:pub.10.1023/b:aire.0000045502.10941.a9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014095928
119 https://doi.org/10.1023/b:aire.0000045502.10941.a9
120 rdf:type schema:CreativeWork
121 sg:pub.10.1186/1475-925x-6-23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004948100
122 https://doi.org/10.1186/1475-925x-6-23
123 rdf:type schema:CreativeWork
124 sg:pub.10.1186/s40537-015-0027-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1023441756
125 https://doi.org/10.1186/s40537-015-0027-y
126 rdf:type schema:CreativeWork
127 https://app.dimensions.ai/details/publication/pub.1077047662 schema:CreativeWork
128 https://doi.org/10.1002/0471448354 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098661481
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1002/sam.11161 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041615658
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/b978-012088469-8.50123-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020508151
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1016/j.csda.2007.05.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005436711
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1016/s0002-9343(97)00244-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009214403
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1016/s0167-9473(96)00027-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034778667
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1080/07421222.1996.11518099 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030081599
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1093/nsr/nwt032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014829674
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1109/2.970578 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061106475
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1109/icde.2011.5767852 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093382456
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1109/icde.2011.5767916 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095048963
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1109/icde.2012.88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093611499
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1136/amiajnl-2011-000681 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009487579
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1136/bmj.310.6987.1122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062771749
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1145/1401890.1401946 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008072501
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1145/1541880.1541882 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030762489
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1145/1541880.1541883 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004686590
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1145/269012.269025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037814739
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1145/276304.276314 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034779247
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1145/276305.276314 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063165004
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1145/335191.335437 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063168484
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1145/375663.375668 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004457232
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1371/journal.pmed.0020267 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033836150
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1515/9781400874668 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043152849
175 rdf:type schema:CreativeWork
176 https://doi.org/10.3926/jiem.2011.v4n2.p168-193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071706223
177 rdf:type schema:CreativeWork
178 https://doi.org/10.4018/jdm.2005010102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030524071
179 rdf:type schema:CreativeWork
180 https://www.grid.ac/institutes/grid.1012.2 schema:alternateName University of Western Australia
181 schema:name School of Computer Science and Software Engineering, University of Western Australia, Perth, Australia
182 rdf:type schema:Organization
 




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


...