Ontology type: schema:Chapter
2019
AUTHORSShafiul Alom Ahmed , Bhabesh Nath , Abhijeet Talukdar
ABSTRACTMany association rule mining techniques have been proposed and most of them are greatly dependent on physical memory. Therefore, the limited amount of physical memory becomes a bottleneck of the existing techniques for large-scale datasets. In this paper, we propose a new approach, (Secondary Storage-Based Frequent Pattern) SbFP-Growth, which is a modification to the FP-Growth algorithm. SbFP-Growth uses secondary storage to store the tree, and therefore, it overcomes the main memory bottleneck at FP-Growth algorithm. By this way, we are able to mine for frequent itemsets from databases of arbitrary size large datasets. Moreover, SbFP-Growth constructs complete tree (SbFP-Tree), i.e., a tree with minimum support count equal to one. It provides the freedom of mining rules for different lower minimum support values without reconstructing the tree from scratch. We can store the SbFP-Tree in secondary storage so that it can be mined later with any minimum support value. More... »
PAGES285-296
Recent Developments in Machine Learning and Data Analytics
ISBN
978-981-13-1279-3
978-981-13-1280-9
http://scigraph.springernature.com/pub.10.1007/978-981-13-1280-9_27
DOIhttp://dx.doi.org/10.1007/978-981-13-1280-9_27
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1106929884
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": [
{
"affiliation": {
"alternateName": "Tezpur University",
"id": "https://www.grid.ac/institutes/grid.45982.32",
"name": [
"Tezpur University"
],
"type": "Organization"
},
"familyName": "Ahmed",
"givenName": "Shafiul Alom",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Tezpur University",
"id": "https://www.grid.ac/institutes/grid.45982.32",
"name": [
"Tezpur University"
],
"type": "Organization"
},
"familyName": "Nath",
"givenName": "Bhabesh",
"id": "sg:person.013356517216.81",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013356517216.81"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Tezpur University",
"id": "https://www.grid.ac/institutes/grid.45982.32",
"name": [
"Tezpur University"
],
"type": "Organization"
},
"familyName": "Talukdar",
"givenName": "Abhijeet",
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1016/j.eswa.2008.03.014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001327543"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-006-0059-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003708906",
"https://doi.org/10.1007/s10618-006-0059-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-006-0059-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003708906",
"https://doi.org/10.1007/s10618-006-0059-1"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.eswa.2007.04.009",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004555974"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/253262.253325",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006848768"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/b:dami.0000005258.31418.83",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019489501",
"https://doi.org/10.1023/b:dami.0000005258.31418.83"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/b:dami.0000040905.52966.1a",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019637134",
"https://doi.org/10.1023/b:dami.0000040905.52966.1a"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-005-0017-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019773065",
"https://doi.org/10.1007/s10618-005-0017-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-005-0017-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019773065",
"https://doi.org/10.1007/s10618-005-0017-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-540-77623-9_21",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021272708",
"https://doi.org/10.1007/978-3-540-77623-9_21"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-005-0268-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024262911",
"https://doi.org/10.1007/s10618-005-0268-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-005-0268-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024262911",
"https://doi.org/10.1007/s10618-005-0268-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-010-0185-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030345982",
"https://doi.org/10.1007/s10618-010-0185-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10489-007-0099-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037042258",
"https://doi.org/10.1007/s10489-007-0099-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10489-007-0099-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037042258",
"https://doi.org/10.1007/s10489-007-0099-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-009-0129-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039123910",
"https://doi.org/10.1007/s10618-009-0129-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-009-0129-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039123910",
"https://doi.org/10.1007/s10618-009-0129-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-009-0129-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039123910",
"https://doi.org/10.1007/s10618-009-0129-2"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/1951365.1951420",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042986763"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-005-0002-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050646167",
"https://doi.org/10.1007/s10618-005-0002-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10618-005-0002-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050646167",
"https://doi.org/10.1007/s10618-005-0002-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1016304305535",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051417652",
"https://doi.org/10.1023/a:1016304305535"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/69.634757",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061213641"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/69.846291",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061213826"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/170036.170072",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1063157468"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/253260.253325",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1098975472"
],
"type": "CreativeWork"
}
],
"datePublished": "2019",
"datePublishedReg": "2019-01-01",
"description": "Many association rule mining techniques have been proposed and most of them are greatly dependent on physical memory. Therefore, the limited amount of physical memory becomes a bottleneck of the existing techniques for large-scale datasets. In this paper, we propose a new approach, (Secondary Storage-Based Frequent Pattern) SbFP-Growth, which is a modification to the FP-Growth algorithm. SbFP-Growth uses secondary storage to store the tree, and therefore, it overcomes the main memory bottleneck at FP-Growth algorithm. By this way, we are able to mine for frequent itemsets from databases of arbitrary size large datasets. Moreover, SbFP-Growth constructs complete tree (SbFP-Tree), i.e., a tree with minimum support count equal to one. It provides the freedom of mining rules for different lower minimum support values without reconstructing the tree from scratch. We can store the SbFP-Tree in secondary storage so that it can be mined later with any minimum support value.",
"editor": [
{
"familyName": "Kalita",
"givenName": "Jugal",
"type": "Person"
},
{
"familyName": "Balas",
"givenName": "Valentina Emilia",
"type": "Person"
},
{
"familyName": "Borah",
"givenName": "Samarjeet",
"type": "Person"
},
{
"familyName": "Pradhan",
"givenName": "Ratika",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-981-13-1280-9_27",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-981-13-1279-3",
"978-981-13-1280-9"
],
"name": "Recent Developments in Machine Learning and Data Analytics",
"type": "Book"
},
"name": "SbFP-Growth: A Step to Remove the Bottleneck of FP-Tree",
"pagination": "285-296",
"productId": [
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-981-13-1280-9_27"
]
},
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"1c9b9fda6dd932c054004bc3683c52bad0acf0f0f32bf126e9082dc0b402e260"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1106929884"
]
}
],
"publisher": {
"location": "Singapore",
"name": "Springer Singapore",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-981-13-1280-9_27",
"https://app.dimensions.ai/details/publication/pub.1106929884"
],
"sdDataset": "chapters",
"sdDatePublished": "2019-04-15T20:35",
"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/v90/0000000001_0000000264/records_8687_00000526.jsonl",
"type": "Chapter",
"url": "http://link.springer.com/10.1007/978-981-13-1280-9_27"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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.10.1007/978-981-13-1280-9_27'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-981-13-1280-9_27'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-981-13-1280-9_27'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-981-13-1280-9_27'
This table displays all metadata directly associated to this object as RDF triples.
160 TRIPLES
23 PREDICATES
46 URIs
20 LITERALS
8 BLANK NODES