Data clustering using analysis of multiple encoding techniques

An embodiment includes building a hierarchical data structure using a hybrid hierarchical clustering process. The hybrid hierarchical clustering process comprises one or more iterations of a level-building process. An embodiment of the level-building process comprises building, by a classical proces...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Kwatra, Shikhar, Yogaraj, Kavitha Hassan, Ghosh, Sudeep, Baughman, Aaron K
Format: Patent
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Kwatra, Shikhar
Yogaraj, Kavitha Hassan
Ghosh, Sudeep
Baughman, Aaron K
description An embodiment includes building a hierarchical data structure using a hybrid hierarchical clustering process. The hybrid hierarchical clustering process comprises one or more iterations of a level-building process. An embodiment of the level-building process comprises building, by a classical processor, a first parent level of a current uppermost level of the hierarchical data structure by clustering classically-encoded clusters of the current uppermost level. The embodiment of the level-building process also comprises identifying, by a quantum processor, a set of candidate clustering options for clustering quantum-encoded clusters of the current uppermost level for a second parent level, including forming each of the set of candidate clustering options in parallel using respective different quantum encoding spaces. The embodiment of the level-building process also comprises building, by the classical processor, the second parent level based on a subset of the candidate clustering options.
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US11921755B2</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US11921755B2</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US11921755B23</originalsourceid><addsrcrecordid>eNrjZLBzSSxJVEjOKS0uSS3KzEtXKC0GkYl5iTmVxZnFCvlpCrmlOSWZBTmpCql5yfkpINmS1OSMvMzC0tRiHgbWtMSc4lReKM3NoOjmGuLsoZtakB-fWlyQmJyal1oSHxpsaGhpZGhuaupkZEyMGgCSXTE8</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Data clustering using analysis of multiple encoding techniques</title><source>esp@cenet</source><creator>Kwatra, Shikhar ; Yogaraj, Kavitha Hassan ; Ghosh, Sudeep ; Baughman, Aaron K</creator><creatorcontrib>Kwatra, Shikhar ; Yogaraj, Kavitha Hassan ; Ghosh, Sudeep ; Baughman, Aaron K</creatorcontrib><description>An embodiment includes building a hierarchical data structure using a hybrid hierarchical clustering process. The hybrid hierarchical clustering process comprises one or more iterations of a level-building process. An embodiment of the level-building process comprises building, by a classical processor, a first parent level of a current uppermost level of the hierarchical data structure by clustering classically-encoded clusters of the current uppermost level. The embodiment of the level-building process also comprises identifying, by a quantum processor, a set of candidate clustering options for clustering quantum-encoded clusters of the current uppermost level for a second parent level, including forming each of the set of candidate clustering options in parallel using respective different quantum encoding spaces. The embodiment of the level-building process also comprises building, by the classical processor, the second parent level based on a subset of the candidate clustering options.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240305&amp;DB=EPODOC&amp;CC=US&amp;NR=11921755B2$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240305&amp;DB=EPODOC&amp;CC=US&amp;NR=11921755B2$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Kwatra, Shikhar</creatorcontrib><creatorcontrib>Yogaraj, Kavitha Hassan</creatorcontrib><creatorcontrib>Ghosh, Sudeep</creatorcontrib><creatorcontrib>Baughman, Aaron K</creatorcontrib><title>Data clustering using analysis of multiple encoding techniques</title><description>An embodiment includes building a hierarchical data structure using a hybrid hierarchical clustering process. The hybrid hierarchical clustering process comprises one or more iterations of a level-building process. An embodiment of the level-building process comprises building, by a classical processor, a first parent level of a current uppermost level of the hierarchical data structure by clustering classically-encoded clusters of the current uppermost level. The embodiment of the level-building process also comprises identifying, by a quantum processor, a set of candidate clustering options for clustering quantum-encoded clusters of the current uppermost level for a second parent level, including forming each of the set of candidate clustering options in parallel using respective different quantum encoding spaces. The embodiment of the level-building process also comprises building, by the classical processor, the second parent level based on a subset of the candidate clustering options.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLBzSSxJVEjOKS0uSS3KzEtXKC0GkYl5iTmVxZnFCvlpCrmlOSWZBTmpCql5yfkpINmS1OSMvMzC0tRiHgbWtMSc4lReKM3NoOjmGuLsoZtakB-fWlyQmJyal1oSHxpsaGhpZGhuaupkZEyMGgCSXTE8</recordid><startdate>20240305</startdate><enddate>20240305</enddate><creator>Kwatra, Shikhar</creator><creator>Yogaraj, Kavitha Hassan</creator><creator>Ghosh, Sudeep</creator><creator>Baughman, Aaron K</creator><scope>EVB</scope></search><sort><creationdate>20240305</creationdate><title>Data clustering using analysis of multiple encoding techniques</title><author>Kwatra, Shikhar ; Yogaraj, Kavitha Hassan ; Ghosh, Sudeep ; Baughman, Aaron K</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US11921755B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Kwatra, Shikhar</creatorcontrib><creatorcontrib>Yogaraj, Kavitha Hassan</creatorcontrib><creatorcontrib>Ghosh, Sudeep</creatorcontrib><creatorcontrib>Baughman, Aaron K</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kwatra, Shikhar</au><au>Yogaraj, Kavitha Hassan</au><au>Ghosh, Sudeep</au><au>Baughman, Aaron K</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Data clustering using analysis of multiple encoding techniques</title><date>2024-03-05</date><risdate>2024</risdate><abstract>An embodiment includes building a hierarchical data structure using a hybrid hierarchical clustering process. The hybrid hierarchical clustering process comprises one or more iterations of a level-building process. An embodiment of the level-building process comprises building, by a classical processor, a first parent level of a current uppermost level of the hierarchical data structure by clustering classically-encoded clusters of the current uppermost level. The embodiment of the level-building process also comprises identifying, by a quantum processor, a set of candidate clustering options for clustering quantum-encoded clusters of the current uppermost level for a second parent level, including forming each of the set of candidate clustering options in parallel using respective different quantum encoding spaces. The embodiment of the level-building process also comprises building, by the classical processor, the second parent level based on a subset of the candidate clustering options.</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_epo_espacenet_US11921755B2
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title Data clustering using analysis of multiple encoding techniques
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T08%3A32%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=Kwatra,%20Shikhar&rft.date=2024-03-05&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS11921755B2%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true