A Deep Clustering Algorithm based on Gaussian Mixture Model
Clustering autonomously learns the implicit cluster structure in the original data without prior knowledge. The effect of ordinary clustering algorithms is not good to cluster high-dimensional data. In this paper, we propose a deep clustering algorithm based on Gaussian mixture model, which combines...
Uloženo v:
| Vydáno v: | Journal of physics. Conference series Ročník 1302; číslo 3; s. 32012 - 32020 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Bristol
IOP Publishing
01.08.2019
|
| Témata: | |
| ISSN: | 1742-6588, 1742-6596 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Clustering autonomously learns the implicit cluster structure in the original data without prior knowledge. The effect of ordinary clustering algorithms is not good to cluster high-dimensional data. In this paper, we propose a deep clustering algorithm based on Gaussian mixture model, which combines two models of stacked auto-encoder and Gaussian mixture model. This algorithm uses the expectation maximization algorithm of reducing dimension data feature to train Gaussian mixture and updates the data cluster so that the data is clustered in the feature space. The experimental results demonstrate that the proposed algorithm improves the clustering accuracy, and verifies the effectiveness of the algorithm. |
|---|---|
| AbstractList | Clustering autonomously learns the implicit cluster structure in the original data without prior knowledge. The effect of ordinary clustering algorithms is not good to cluster high-dimensional data. In this paper, we propose a deep clustering algorithm based on Gaussian mixture model, which combines two models of stacked auto-encoder and Gaussian mixture model. This algorithm uses the expectation maximization algorithm of reducing dimension data feature to train Gaussian mixture and updates the data cluster so that the data is clustered in the feature space. The experimental results demonstrate that the proposed algorithm improves the clustering accuracy, and verifies the effectiveness of the algorithm. |
| Author | Yang, Xiaofei Lin, Xianghong Li, Ying |
| Author_xml | – sequence: 1 givenname: Xianghong surname: Lin fullname: Lin, Xianghong email: linxh@nwnu.edu.cn organization: College of Computer Science and Engineering, Northwest Normal University , China – sequence: 2 givenname: Xiaofei surname: Yang fullname: Yang, Xiaofei organization: College of Computer Science and Engineering, Northwest Normal University , China – sequence: 3 givenname: Ying surname: Li fullname: Li, Ying organization: College of Computer Science and Engineering, Northwest Normal University , China |
| BookMark | eNqNkF1LwzAUhoNMcJv-BgPeCbX5aNoU8WJUncqGgnoduiSdGV1Tkxb039tSmSiCnpscyPPmDc8EjCpbaQCOMTrDiPMQJxEJYpbGIaaIhDRElCBM9sB4dzPa7ZwfgIn3G4RoN8kYnM_gpdY1zMrWN9qZag1n5do607xs4Sr3WkFbwXneem_yCi7NW9M6DZdW6fIQ7Bd56fXR5zkFz9dXT9lNsLif32azRSAp4SRIGZcsVglWiOE0lpwUNFISrVQUaaVwERGZppJHUulIFYToiBcxS4jO0UrLlE7ByfBu7exrq30jNrZ1VVcpCIuTFLOE9dTFQElnvXe6ENI0eWNs1bjclAIj0fsSvQnRWxG9L0HF4KvLJz_ytTPb3L3_I3k6JI2tv75295A9fgdFrYoOpr_Af1V8ANa5i4E |
| CitedBy_id | crossref_primary_10_1038_s41598_024_68974_8 crossref_primary_10_2118_212833_PA crossref_primary_10_1186_s40537_024_01015_3 crossref_primary_10_1016_j_procs_2025_03_032 |
| Cites_doi | 10.1038/nature14539 10.1145/331499.331504 10.1109/CSE-EUC.2017.215 10.1109/TIP.2010.2049235 10.1561/2200000006 10.1198/016214502760047131 10.1162/neco.2006.18.7.1527 |
| ContentType | Journal Article |
| Copyright | Published under licence by IOP Publishing Ltd 2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: Published under licence by IOP Publishing Ltd – notice: 2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | O3W TSCCA AAYXX CITATION 8FD 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO H8D HCIFZ L7M P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS |
| DOI | 10.1088/1742-6596/1302/3/032012 |
| DatabaseName | Institute of Physics Open Access Journal Titles IOPscience (Open Access) CrossRef Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central ProQuest Technology Collection ProQuest One ProQuest Central Korea Aerospace Database SciTech Premium Collection Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China |
| DatabaseTitle | CrossRef Publicly Available Content Database Advanced Technologies & Aerospace Collection Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences Aerospace Database ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic Advanced Technologies Database with Aerospace ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: O3W name: Institute of Physics Open Access Journal Titles url: http://iopscience.iop.org/ sourceTypes: Enrichment Source Publisher – sequence: 2 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics |
| DocumentTitleAlternate | A Deep Clustering Algorithm based on Gaussian Mixture Model |
| EISSN | 1742-6596 |
| ExternalDocumentID | 10_1088_1742_6596_1302_3_032012 JPCS_1302_3_032012 |
| GroupedDBID | 1JI 29L 2WC 4.4 5B3 5GY 5PX 5VS 7.Q AAJIO AAJKP ABHWH ACAFW ACHIP AEFHF AEJGL AFKRA AFYNE AIYBF AKPSB ALMA_UNASSIGNED_HOLDINGS ARAPS ASPBG ATQHT AVWKF AZFZN BENPR BGLVJ CCPQU CEBXE CJUJL CRLBU CS3 DU5 E3Z EBS EDWGO EQZZN F5P FRP GROUPED_DOAJ GX1 HCIFZ HH5 IJHAN IOP IZVLO J9A KNG KQ8 LAP N5L N9A O3W OK1 P2P PIMPY PJBAE RIN RNS RO9 ROL SY9 T37 TR2 TSCCA UCJ W28 XSB ~02 AAYXX AEINN AFFHD CITATION OVT PHGZM PHGZT PQGLB 8FD 8FE 8FG ABUWG AZQEC DWQXO H8D L7M P62 PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c3282-958c56d71d05196c82f34dc0bd44edd1f42c99c84cde4df22e48f6572ea0bec93 |
| IEDL.DBID | PIMPY |
| ISSN | 1742-6588 |
| IngestDate | Fri Jul 25 07:54:49 EDT 2025 Sat Nov 29 01:47:25 EST 2025 Tue Nov 18 22:35:40 EST 2025 Wed Aug 21 03:40:55 EDT 2024 Fri Jan 08 09:41:16 EST 2021 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Language | English |
| License | Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3282-958c56d71d05196c82f34dc0bd44edd1f42c99c84cde4df22e48f6572ea0bec93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://www.proquest.com/publiccontent/docview/2567915759?pq-origsite=%requestingapplication% |
| PQID | 2567915759 |
| PQPubID | 4998668 |
| PageCount | 9 |
| ParticipantIDs | crossref_primary_10_1088_1742_6596_1302_3_032012 crossref_citationtrail_10_1088_1742_6596_1302_3_032012 proquest_journals_2567915759 iop_journals_10_1088_1742_6596_1302_3_032012 |
| PublicationCentury | 2000 |
| PublicationDate | 20190801 |
| PublicationDateYYYYMMDD | 2019-08-01 |
| PublicationDate_xml | – month: 08 year: 2019 text: 20190801 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Bristol |
| PublicationPlace_xml | – name: Bristol |
| PublicationTitle | Journal of physics. Conference series |
| PublicationTitleAlternate | J. Phys.: Conf. Ser |
| PublicationYear | 2019 |
| Publisher | IOP Publishing |
| Publisher_xml | – name: IOP Publishing |
| References | Jain (JPCS_1302_3_032012bib4) 1999; 31 Bengio (JPCS_1302_3_032012bib9) 2007 Lecun (JPCS_1302_3_032012bib3) 2015; 521 Gheisari (JPCS_1302_3_032012bib2) 2017 Hinton (JPCS_1302_3_032012bib11) 2006; 18 Fraley (JPCS_1302_3_032012bib15) 2002; 97 Xie (JPCS_1302_3_032012bib6) 2015 Bengio (JPCS_1302_3_032012bib8) 2009; 2 Mclachlan (JPCS_1302_3_032012bib13) 2004 Bishop (JPCS_1302_3_032012bib12) 1995; 12 Yang (JPCS_1302_3_032012bib16) 2010; 19 Torre (JPCS_1302_3_032012bib5) 2006 Larochelle (JPCS_1302_3_032012bib10) 2009; 10 Ngiam (JPCS_1302_3_032012bib7) 2011 Yi-Ou (JPCS_1302_3_032012bib1) 2017; 46 Dilokthanakul (JPCS_1302_3_032012bib14) 2016 |
| References_xml | – volume: 521 start-page: 436 year: 2015 ident: JPCS_1302_3_032012bib3 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – year: 2006 ident: JPCS_1302_3_032012bib5 article-title: Discriminative cluster analysis – year: 2016 ident: JPCS_1302_3_032012bib14 article-title: Deep unsupervised clustering with gaussian mixture variational autoencoders J – year: 2015 ident: JPCS_1302_3_032012bib6 article-title: Unsupervised deep embedding for clustering analysis – volume: 31 start-page: 264 year: 1999 ident: JPCS_1302_3_032012bib4 article-title: Data clustering: a review publication-title: Acm Computing Surveys doi: 10.1145/331499.331504 – start-page: 153 year: 2007 ident: JPCS_1302_3_032012bib9 article-title: Greedy layer-wise training of deep networks – year: 2004 ident: JPCS_1302_3_032012bib13 – start-page: 689 year: 2011 ident: JPCS_1302_3_032012bib7 article-title: Multimodal deep learning – volume: 46 start-page: 913 year: 2017 ident: JPCS_1302_3_032012bib1 article-title: Deep learning in NLP: methods and applications publication-title: Journal of University of Electronic Science & Technology of China – year: 2017 ident: JPCS_1302_3_032012bib2 article-title: A survey on deep learning in big data doi: 10.1109/CSE-EUC.2017.215 – volume: 19 start-page: 2761 year: 2010 ident: JPCS_1302_3_032012bib16 article-title: Image clustering using local discriminant models and global integration publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2010.2049235 – volume: 2 start-page: 1 year: 2009 ident: JPCS_1302_3_032012bib8 article-title: Learning Deep Architectures for AI publication-title: Foundations and Trends® in Machine Learning doi: 10.1561/2200000006 – volume: 97 start-page: 611 year: 2002 ident: JPCS_1302_3_032012bib15 article-title: Model-based clustering, discriminant analysis, and density estimation publication-title: Journal of the American statistical Association doi: 10.1198/016214502760047131 – volume: 18 start-page: 1527 year: 2006 ident: JPCS_1302_3_032012bib11 article-title: A fast learning algorithm for deep belief nets publication-title: Neural computation doi: 10.1162/neco.2006.18.7.1527 – volume: 10 start-page: 1 year: 2009 ident: JPCS_1302_3_032012bib10 article-title: Exploring strategies for training deep neural networks publication-title: Journal of machine learning research – volume: 12 year: 1995 ident: JPCS_1302_3_032012bib12 article-title: Neural networks for pattern recognition publication-title: Agricultural Engineering International the Cigr Journal of Scientific Research & Development Manuscript Pm |
| SSID | ssj0033337 |
| Score | 2.266775 |
| Snippet | Clustering autonomously learns the implicit cluster structure in the original data without prior knowledge. The effect of ordinary clustering algorithms is not... |
| SourceID | proquest crossref iop |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 32012 |
| SubjectTerms | Algorithms Clustering Coders Physics Probabilistic models |
| SummonAdditionalLinks | – databaseName: Institute of Physics Open Access Journal Titles dbid: O3W link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dS8MwEA86FXzxW5xOCeijdW2Spgk-jfmF6BRU3Ftpk1QHsxvrJv755tZWKSJDsC_tw10bftfmrtzHD6GjJPIjRalyrKtSDvPiyJGQKaSeJi6NZaxzS98EnY7odmWlF2YwLLb-E3uZDwrOISwK4kTTxtDE4b7kTUi6NWkTOMCBaHiBCt-Hsr47-lzuxtQeQd4UCUpClDVev9-o4qHm7Sp-bNNT33Ox-h-rXkMrReSJW7nGOpoz6QZamlaAqmwTnbbwmTFD3O5PYHaC9Wi41X8ZjHrj1zcMvk7jQYovo0kGbZf4tvcBuQcMXGr9LfR0cf7YvnIKZgVHUfuP5UhfKJ_rwNMQwXElSEKZVm6sGTNaewkjSkolmNKG6YQQw0TC_YCYyLVGl3Qb1dJBanYQFgoYwWXgMg1dXEEUiZhbl8cTESREqDriJZqhKsaOA_tFP5ymv4UIAZkQkIHUGglpmCNTR-6X4jCfvDFb5djCHxZfYTZb_LAifn3ffqhKhEOd1FGjtP63qA0UA-kBuenu3565h5btSeYlhA1UG48mZh8tqvdxLxsdTF_dTwuC4gk priority: 102 providerName: IOP Publishing |
| Title | A Deep Clustering Algorithm based on Gaussian Mixture Model |
| URI | https://iopscience.iop.org/article/10.1088/1742-6596/1302/3/032012 https://www.proquest.com/docview/2567915759 |
| Volume | 1302 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIOP databaseName: Institute of Physics Open Access Journal Titles customDbUrl: eissn: 1742-6596 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0033337 issn: 1742-6588 databaseCode: O3W dateStart: 20040101 isFulltext: true titleUrlDefault: http://iopscience.iop.org/ providerName: IOP Publishing – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1742-6596 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0033337 issn: 1742-6588 databaseCode: P5Z dateStart: 20040801 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1742-6596 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0033337 issn: 1742-6588 databaseCode: BENPR dateStart: 20040801 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1742-6596 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0033337 issn: 1742-6588 databaseCode: PIMPY dateStart: 20040801 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Nb9MwFH-i7SZx4RtRGJUldiRKYjuJLQ6oKxsbYl00mDZ2iRLbYZW6NjQt4s-fX-MwVUjsshxySJ4tJe_l_ey8jx_AbplHuWJMeRaqlMfDIvckRgpZqGnAClnoRtNfk_FYXFzI1JVH1y6tsvWJa0fddHvGvG3rhH09V_jH3LdAncgQySU_Vr885JDCWKsj1OhADxtvBV3opUfH6Y_WMzN7JE2BJPUs8oo238tuAt01GfsYyfOZj8TiId1Aq85kXv3jstc4dPD4fp_gCTxy61EybAzoKTwws2ewvc4LVfVz-DAkn4ypyGi6wo4KFufIcPrTzrO8uiaIgJrMZ-RzvqqxGJMcT_5gRIIgw9r0BZwd7H8fHXqOb8FTzO68PBkJFcU6CTWu62IlaMm4VkGhOTdahyWnSkoluNKG65JSw0UZRwk1eWBNQbKX0J3NZ-YVEKGQJ1wmAddY25XkuShiC4RxKZKSCtWHuH2vmXLNyJETY5qtg-JCZKiQDBWCATeasaxRSB-CvwOrph_H3UPeW8Vl7tus7xZ_tyH-JR1925TIKl32YadV8q3orU5f___2G3hoJ5JNIuEOdJeLlXkLW-r3clIvBtDb2x-npwPonLBze06jy4Ez5RuRofWk |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3fT9swED6xsml7AbYxrQOGJba3RU1sN7GFJlSVMQptVWlMgieT2A5U6tqMtAP-qf2N-JpkqJo0nnhYHpNzlOTO99m5Hx_AhzRuxpox7Tmo0h4PktiTGClkgaE-S2RiCk13o35fnJ7KwRL8rmphMK2y8olzR20mGv-RNxw0RzJAOsm97KeHrFEYXa0oNAqzOLa3127Lln_u7Dv9fqT04MtJ-9ArWQU8zdz-wpNNoZuhiQKDq5dQC5oybrSfGM6tMUHKqZZSC66N5Sal1HKRhs2I2th3L4zNl5zLX-bO2P0aLA86vcFZ5fuZO6KiBJN6DttFlVHmtpnlORk2MFbYYA2kLg_oAh4-GU6yv0BhjnQHq__bN1qDlXJNTVrFJHgJS3b8Cp7Nc1t1_hp2W2Tf2oy0RzPsCuGwmrRGF-65p5c_CKK4IZMx-RrPciwoJb3hDUZVCLLEjdbh-6M8-RuojSdj-xaI0Mh1LiOfG6xPi-JYJKED8zAVUUqFrkNYaU7psqE68nqM1DywL4RClStUOQYNqWKqUHkd_D8Ds6KnyMNDPjnTUKV_yR8W31kQPxq0vy1KqMykddiszOhe9N6G3v378jY8PzzpdVW30z_egBfuprJIjNyE2vRqZrfgqf41HeZX78tJQuD8sW3uDleCRHA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dT9swED9R2CZegG0gOmBY2h6XJbGdxBZPVaEM1nWVtml9sxLbYZVKU_UD8efP1yRFFUJo0vIURXeJdefcnXUfP4CPeRqlmjHtOVelPR5mqScxU8hCQwOWycyUmu4mvZ4YDGR_AzqrXphiUpn-z-62HBRcirAqiBO-i6GpF0cy9jHp5jMfMcBD6k9M3oAtHFeCSAbf2e_aIjN3JWVjJDIKUdd5Pf2yNS_VcCt5ZKqX_qez-79Wvgc7VQRKWiXXa9iw4zfwclkJqmdv4axFzq2dkPZogTMUnGcjrdFNMR3O_9wS9HmGFGNymS5m2H5Jvg3vMQdBEFNttA-_Ohc_21-8CmHB08ydtTwZCR3FJgkNRnKxFjRn3OggM5xbY8KcUy2lFlwby01OqeUij6OE2jRwypfsADbHxdgeAhEakcFlEnCD3VxJmoosdq4vzkWSU6GbENcSVboaP44oGCO1TIMLoVA6CqWDKTaqmCql04RgxTgpJ3A8z_LJqUBVf-PsefIPa-TX_faPdQrlNNSE43oHPJC6gDGRIYKcvvu3b57Cq_55R3Wvel-PYNs9kWVV4TFszqcLewIv9N18OJu-X-7kv3vi52g |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Deep+Clustering+Algorithm+based+on+Gaussian+Mixture+Model&rft.jtitle=Journal+of+physics.+Conference+series&rft.au=Lin%2C+Xianghong&rft.au=Yang%2C+Xiaofei&rft.au=Li%2C+Ying&rft.date=2019-08-01&rft.pub=IOP+Publishing&rft.issn=1742-6588&rft.eissn=1742-6596&rft.volume=1302&rft.issue=3&rft_id=info:doi/10.1088%2F1742-6596%2F1302%2F3%2F032012 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1742-6588&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1742-6588&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1742-6588&client=summon |