Taxonomy grooming algorithm ‐ An autodidactic domain specific dimensionality reduction approach for fast clustering of social media text data
Social media being the most eminent source toward the growth of big data is important for information retrieval‐based applications to improve the efficiency in proportional to the volume it must deal with. One way to achieve better performance is to upgrade the processing capacity and the alternativ...
Gespeichert in:
| Veröffentlicht in: | Concurrency and computation Jg. 34; H. 11 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, USA
John Wiley & Sons, Inc
15.05.2022
Wiley Subscription Services, Inc |
| Schlagworte: | |
| ISSN: | 1532-0626, 1532-0634 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Social media being the most eminent source toward the growth of big data is important for information retrieval‐based applications to improve the efficiency in proportional to the volume it must deal with. One way to achieve better performance is to upgrade the processing capacity and the alternative option is to improve the processing methodology. The latter can be achieved using smarter processing techniques and/or better algorithms. Reducing the data volume that needs to be processed is a good strategy and it can be achieved by extracting only the relevant information via user segmentation by adopting an appropriate clustering technique. However, while dealing with text content, clustering algorithms do suffer due to the very high dimensions to be dealt with. Since the domain‐specific aspects are getting lost while applying traditional dimensionality reduction approaches, it is important to device an alternate strategy. This work proposes a taxonomy grooming algorithm (TGA), an autodidactic domain‐specific dimensionality reduction approach, for fast clustering of social media text data. Our experiment results are very promising and the dimensionality reduction using TGA resulted in better results in comparison with the traditional dimensionality reduction approaches. |
|---|---|
| AbstractList | Social media being the most eminent source toward the growth of big data is important for information retrieval‐based applications to improve the efficiency in proportional to the volume it must deal with. One way to achieve better performance is to upgrade the processing capacity and the alternative option is to improve the processing methodology. The latter can be achieved using smarter processing techniques and/or better algorithms. Reducing the data volume that needs to be processed is a good strategy and it can be achieved by extracting only the relevant information via user segmentation by adopting an appropriate clustering technique. However, while dealing with text content, clustering algorithms do suffer due to the very high dimensions to be dealt with. Since the domain‐specific aspects are getting lost while applying traditional dimensionality reduction approaches, it is important to device an alternate strategy. This work proposes a taxonomy grooming algorithm (TGA), an autodidactic domain‐specific dimensionality reduction approach, for fast clustering of social media text data. Our experiment results are very promising and the dimensionality reduction using TGA resulted in better results in comparison with the traditional dimensionality reduction approaches. |
| Author | Sreekumar, A. Renjith, Shini Jathavedan, M. |
| Author_xml | – sequence: 1 givenname: Shini orcidid: 0000-0003-1088-0825 surname: Renjith fullname: Renjith, Shini email: shinirenjith@gmail.com organization: Mar Baselios College of Engineering and Technology – sequence: 2 givenname: A. surname: Sreekumar fullname: Sreekumar, A. organization: Cochin University of Science and Technology – sequence: 3 givenname: M. surname: Jathavedan fullname: Jathavedan, M. organization: Cochin University of Science and Technology |
| BookMark | eNp1kMtOwzAQRS0EEuUh8QmW2LBJ8SOJm2VVlYeEBIuyjqZ-tK6SONiOoDv-AL6RLyGhiAWClceac2fm3iO037hGI3RGyZgSwi5lq8f5hIs9NKIZZwnJebr_U7P8EB2FsCGEUsLpCL0t4MU1rt7ilXeuts0KQ7Vy3sZ1jT9e3_G0wdBFp6wCGa3EytVgGxxaLa0Z_rbWTbCugcrGLfZadT3nelXbegdyjY3z2ECIWFZdiNoPO5zBwUkLFa61soCjfolYQYQTdGCgCvr0-z1Gj1fzxewmubu_vp1N7xLJCi4Sni-JSOVSGJpBVjCll1otU661nBSMCJATXpi0Ny8FlSBYJggb-lxmRgngx-h8N7c_8qnTIZYb1_neRChZntGMMJIWPTXeUdK7ELw2pbQRBnvRg61KSsoh9LIPvRxC7wUXvwSttzX47V9oskOfbaW3_3Ll7GH-xX8CKPeWqw |
| CitedBy_id | crossref_primary_10_1007_s42452_024_06443_7 |
| Cites_doi | 10.1007/978-0-387-30164-8_826 10.7551/mitpress/7287.003.0018 10.1016/0377-0427(87)90125-7 10.1145/2124295.2124308 10.1016/j.matpr.2020.01.110 10.1016/j.patrec.2014.09.008 10.1007/978-981-15-3514-7_78 10.1109/TPAMI.1979.4766909 10.1016/j.eswa.2019.05.030 10.1007/978-1-4615-5725-8_7 10.1007/978-981-15-5558-9_45 10.1016/j.eswa.2014.11.038 10.1002/cpe.6359 10.1016/j.csl.2004.05.007 10.1016/j.procs.2015.02.026 10.1080/03610927408827101 10.1007/3-540-36175-8_7 10.1109/ICDM.2003.1250972 10.1016/S0957-4174(02)00185-9 10.1016/j.ins.2009.02.019 10.1016/j.eswa.2014.10.023 10.1075/cilt.189.35deb 10.1145/2766462.2767755 10.3115/1621445.1621458 10.1126/science.295.5552.7a 10.3115/981732.981751 10.1037/h0071325 10.1016/j.patcog.2017.09.045 10.1109/IJCNN.1998.685895 10.1109/RAICS.2018.8635080 10.1016/j.jocs.2013.11.007 10.1145/219717.219748 10.1016/j.neucom.2017.11.019 10.1016/S2095-3119(12)60064-1 10.1016/j.asoc.2016.01.019 10.1016/j.future.2017.12.005 10.1080/13102818.2014.949045 10.1016/j.csl.2004.05.004 10.1016/j.asoc.2014.11.015 10.1007/978-981-15-7234-0_36 10.1145/2872427.2883037 10.1016/j.ipm.2019.102078 10.1108/eb026526 10.1007/11424918_14 10.1016/j.knosys.2014.11.028 10.1037/h0054116 |
| ContentType | Journal Article |
| Copyright | 2022 John Wiley & Sons Ltd. 2022 John Wiley & Sons, Ltd. |
| Copyright_xml | – notice: 2022 John Wiley & Sons Ltd. – notice: 2022 John Wiley & Sons, Ltd. |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1002/cpe.6837 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | CrossRef Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1532-0634 |
| EndPage | n/a |
| ExternalDocumentID | 10_1002_cpe_6837 CPE6837 |
| Genre | article |
| GroupedDBID | .3N .DC .GA 05W 0R~ 10A 1L6 1OC 33P 3SF 3WU 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHHS AAHQN AAMNL AANLZ AAONW AAXRX AAYCA AAZKR ABCQN ABCUV ABEML ABIJN ACAHQ ACCFJ ACCZN ACPOU ACSCC ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADOZA ADXAS ADZMN ADZOD AEEZP AEIGN AEIMD AEQDE AEUQT AEUYR AFBPY AFFPM AFGKR AFPWT AFWVQ AHBTC AITYG AIURR AIWBW AJBDE AJXKR ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ATUGU AUFTA AZBYB BAFTC BDRZF BFHJK BHBCM BMNLL BROTX BRXPI BY8 CS3 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM EBS F00 F01 F04 F5P G-S G.N GNP GODZA HGLYW HHY HZ~ IX1 JPC KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A O66 O9- OIG P2W P2X P4D PQQKQ Q.N Q11 QB0 QRW R.K ROL RWI RX1 SUPJJ TN5 UB1 V2E W8V W99 WBKPD WIH WIK WOHZO WQJ WRC WXSBR WYISQ WZISG XG1 XV2 ~IA ~WT AAYXX ADMLS AEYWJ AGHNM AGYGG CITATION O8X 7SC 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c2937-36b074cb7f15a592debedb43eec89207ac839f4063c71ca725702b43e3c5fd7a3 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000749260800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1532-0626 |
| IngestDate | Sun Nov 09 06:00:41 EST 2025 Tue Nov 18 21:38:38 EST 2025 Sat Nov 29 01:41:28 EST 2025 Wed Jan 22 16:26:06 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2937-36b074cb7f15a592debedb43eec89207ac839f4063c71ca725702b43e3c5fd7a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-1088-0825 |
| PQID | 2651502049 |
| PQPubID | 2045170 |
| PageCount | 19 |
| ParticipantIDs | proquest_journals_2651502049 crossref_citationtrail_10_1002_cpe_6837 crossref_primary_10_1002_cpe_6837 wiley_primary_10_1002_cpe_6837_CPE6837 |
| PublicationCentury | 2000 |
| PublicationDate | 15 May 2022 |
| PublicationDateYYYYMMDD | 2022-05-15 |
| PublicationDate_xml | – month: 05 year: 2022 text: 15 May 2022 day: 15 |
| PublicationDecade | 2020 |
| PublicationPlace | Hoboken, USA |
| PublicationPlace_xml | – name: Hoboken, USA – name: Hoboken |
| PublicationTitle | Concurrency and computation |
| PublicationYear | 2022 |
| Publisher | John Wiley & Sons, Inc Wiley Subscription Services, Inc |
| Publisher_xml | – name: John Wiley & Sons, Inc – name: Wiley Subscription Services, Inc |
| References | 1933; 24 1995; 38 2015; 75 2019; 57 2008; 9 1943; 38 2018; 82 2014; 28 2012; 11 1974; 3 2020a; 1133 2015; 46 2014; 5 2000 2015; 42 2016; 43 1998; 98 2018; 76 2010; 5 2021b; 33 2012 2011 2002; 295 2015; 52 1998 1997 2007 1995 2009; 179 1994 2005 2004 2003 2020c; 1245 2020b; 672 2021a; 5 1972; 28 1999 1979; PAMI‐1 2018; 275 1987; 20 2015; 27 2004; 18 2003; 24 2020; 27 2005; 1 2016 2005; 17 2019; 134 e_1_2_9_31_1 e_1_2_9_50_1 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_56_1 e_1_2_9_12_1 e_1_2_9_33_1 e_1_2_9_54_1 Hung C (e_1_2_9_22_1) 2005; 1 e_1_2_9_39_1 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_58_1 Renjith S (e_1_2_9_14_1) 2020; 1133 d'Aspremont A (e_1_2_9_18_1) 2005; 17 e_1_2_9_41_1 e_1_2_9_20_1 Manning CD (e_1_2_9_2_1) 1999 e_1_2_9_45_1 Lin D (e_1_2_9_7_1) 1998; 98 e_1_2_9_24_1 e_1_2_9_43_1 e_1_2_9_6_1 e_1_2_9_4_1 Renjith S (e_1_2_9_53_1) 2021; 5 Renjith S (e_1_2_9_52_1) 2020; 1245 Maaten L (e_1_2_9_19_1) 2008; 9 e_1_2_9_26_1 e_1_2_9_28_1 e_1_2_9_47_1 e_1_2_9_30_1 e_1_2_9_51_1 e_1_2_9_11_1 e_1_2_9_34_1 e_1_2_9_57_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_55_1 Chali Y (e_1_2_9_49_1) 2005 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_17_1 e_1_2_9_36_1 Basavaraju M (e_1_2_9_48_1) 2010; 5 e_1_2_9_42_1 e_1_2_9_40_1 e_1_2_9_21_1 e_1_2_9_46_1 e_1_2_9_23_1 e_1_2_9_44_1 Leacock C (e_1_2_9_8_1) 1998 e_1_2_9_5_1 e_1_2_9_3_1 e_1_2_9_9_1 e_1_2_9_25_1 e_1_2_9_27_1 e_1_2_9_29_1 |
| References_xml | – volume: 1133 start-page: 1047 year: 2020a end-page: 1065 article-title: A comparative analysis of clustering quality based on internal validation indices for dimensionally reduced social media data publication-title: Adv Intell Syst Comput – volume: 295 start-page: 7 year: 2002 end-page: 7 article-title: The isomap algorithm and topological stability publication-title: Science – start-page: 280 year: 2005 end-page: 291 – volume: 98 start-page: 296 year: 1998 end-page: 304 article-title: An information‐theoretic definition of similarity publication-title: Icml – volume: 28 start-page: S44 year: 2014 end-page: S48 article-title: Clustering performance comparison using K‐means and expectation maximization algorithms publication-title: Biotechnol Biotechnol Equip – volume: 18 start-page: 301 year: 2004 end-page: 317 article-title: Word sense disambiguation of WordNet glosses publication-title: Comput Speech Lang – volume: 1245 start-page: 407 year: 2020c end-page: 414 article-title: A sentiment‐based recommender system framework for social media big data using open‐source tech stack publication-title: Adv Intell Syst Comput – start-page: 101 year: 1998 end-page: 116 – volume: 275 start-page: 2444 year: 2018 end-page: 2458 article-title: Corpus‐based topic diffusion for short text clustering publication-title: Neurocomputing – start-page: 120 year: 2005 end-page: 132 – volume: 5 start-page: 297 year: 2021a end-page: 307 article-title: SMaRT: a framework for social media based recommender for tourism publication-title: Trans Comput Sci Comput Intell – year: 1994 – year: 1998 – volume: 5 start-page: 156 year: 2014 end-page: 169 article-title: A three‐stage unsupervised dimension reduction method for text clustering publication-title: J Comput Sci – volume: 27 start-page: 627 issue: 1 year: 2020 end-page: 633 article-title: Performance evaluation of clustering algorithms for varying cardinality and dimensionality of data sets publication-title: Mater Today Proc – volume: 17 start-page: 41 year: 2005 end-page: 48 article-title: A direct formulation for sparse PCA using semidefinite programming publication-title: Adv Neural Inf Proces Syst – volume: 75 start-page: 152 year: 2015 end-page: 160 article-title: TESC: an approach to text classification using semi‐supervised clustering publication-title: Knowl‐Based Syst – volume: 38 start-page: 39 year: 1995 end-page: 41 article-title: WordNet: a lexical database for English publication-title: Commun ACM – start-page: 353 year: 2000 – volume: 24 start-page: 417 year: 1933 end-page: 441 article-title: Analysis of a complex of statistical variables into principal components publication-title: J Educ Psychol – year: 2004 – year: 1997 – volume: 5 start-page: 15 year: 2010 end-page: 25 article-title: A novel method of spam mail detection using text based clustering approach publication-title: Int J Comput Appl – volume: PAMI‐1 start-page: 224 year: 1979 end-page: 227 article-title: A cluster separation measure publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 46 start-page: 314 year: 2015 end-page: 320 article-title: A lexical approach for text categorization of medical documents publication-title: Procedia Comput Sci – volume: 43 start-page: 20 year: 2016 end-page: 34 article-title: Opposition chaotic fitness mutation based adaptive inertia weight BPSO for feature selection in text clustering publication-title: Appl Soft Comput – volume: 42 start-page: 3105 year: 2015 end-page: 3114 article-title: Hybrid dimension reduction by integrating feature selection with feature extraction method for text clustering publication-title: Expert Syst Appl – start-page: 63 year: 2003 end-page: 74 – volume: 672 start-page: 499 year: 2020b end-page: 512 article-title: Pragmatic evaluation of the impact of dimensionality reduction in the performance of clustering algorithms publication-title: Lect Notes Electr Eng – volume: 9 start-page: 2579 year: 2008 end-page: 2605 article-title: Visualizing data using t‐SNE publication-title: J Mach Learn Res – volume: 42 start-page: 2264 year: 2015 end-page: 2275 article-title: A semantic approach for text clustering using WordNet and lexical chains publication-title: Expert Syst Appl – volume: 11 start-page: 752 year: 2012 end-page: 759 article-title: Agricultural ontology based feature optimization for agricultural text clustering publication-title: J Integr Agric – year: 2007 – volume: 27 start-page: 269 year: 2015 end-page: 278 article-title: A novel incremental conceptual hierarchical text clustering method using CFu‐tree publication-title: Appl Soft Comput – year: 2003 – volume: 52 start-page: 25 year: 2015 end-page: 31 article-title: Interactive textual feature selection for consensus clustering publication-title: Pattern Recogn Lett – volume: 3 start-page: 1 year: 1974 end-page: 27 article-title: A dendrite method for cluster analysis publication-title: Commun Stat Theory Methods – year: 2016 – year: 2012 – volume: 28 start-page: 11 year: 1972 end-page: 21 article-title: A statistical interpretation of term specificity and its application in retrieval publication-title: J Doc – volume: 33 year: 2021b article-title: SemRec – an efficient ensemble recommender with sentiment based clustering for social media text corpus publication-title: Concurrency Computat Pract Exper – volume: 18 start-page: 253 year: 2004 end-page: 273 article-title: Unsupervised word sense disambiguation using WordNet relatives publication-title: Comput Speech Lang – start-page: 963 year: 2011 end-page: 968 – volume: 76 start-page: 691 year: 2018 end-page: 703 article-title: Concept decompositions for short text clustering by identifying word communities publication-title: Pattern Recogn – volume: 179 start-page: 2249 year: 2009 end-page: 2262 article-title: Exploiting noun phrases and semantic relationships for text document clustering publication-title: Inf Sci – volume: 134 start-page: 192 year: 2019 end-page: 200 article-title: Text document clustering using spectral clustering algorithm with particle swarm optimization publication-title: Expert Syst Appl – volume: 20 start-page: 53 year: 1987 end-page: 65 article-title: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis publication-title: J Comput Appl Math – volume: 1 start-page: 127 year: 2005 end-page: 142 article-title: Neural network based document clustering using WordNet ontologies publication-title: Int J Hybrid Intell Syst – volume: 38 start-page: 476 year: 1943 end-page: 506 article-title: The description of personality: basic traits resolved into clusters publication-title: J Abnorm Soc Psychol – volume: 24 start-page: 351 year: 2003 end-page: 363 article-title: Empirical comparison of fast partitioning‐based clustering algorithms for large data sets publication-title: Expert Syst Appl – year: 1995 – volume: 57 issue: 1 year: 2019 article-title: An extensive study on the evolution of context‐aware personalized travel recommender systems publication-title: Inf Process Manag – volume: 82 start-page: 190 year: 2018 end-page: 199 article-title: Link based BPSO for feature selection in big data text clustering publication-title: Futur Gener Comput Syst – year: 1999 – ident: e_1_2_9_55_1 doi: 10.1007/978-0-387-30164-8_826 – volume-title: Combining Local Context and WordNet Similarity for Word Sense Identification year: 1998 ident: e_1_2_9_8_1 doi: 10.7551/mitpress/7287.003.0018 – ident: e_1_2_9_56_1 – ident: e_1_2_9_11_1 doi: 10.1016/0377-0427(87)90125-7 – ident: e_1_2_9_50_1 doi: 10.1145/2124295.2124308 – ident: e_1_2_9_44_1 doi: 10.1016/j.matpr.2020.01.110 – ident: e_1_2_9_37_1 doi: 10.1016/j.patrec.2014.09.008 – volume: 1133 start-page: 1047 year: 2020 ident: e_1_2_9_14_1 article-title: A comparative analysis of clustering quality based on internal validation indices for dimensionally reduced social media data publication-title: Adv Intell Syst Comput doi: 10.1007/978-981-15-3514-7_78 – ident: e_1_2_9_13_1 doi: 10.1109/TPAMI.1979.4766909 – start-page: 280 volume-title: Lecture Notes in Computer Science year: 2005 ident: e_1_2_9_49_1 – ident: e_1_2_9_36_1 doi: 10.1016/j.eswa.2019.05.030 – ident: e_1_2_9_5_1 – ident: e_1_2_9_16_1 doi: 10.1007/978-1-4615-5725-8_7 – ident: e_1_2_9_15_1 doi: 10.1007/978-981-15-5558-9_45 – ident: e_1_2_9_30_1 doi: 10.1016/j.eswa.2014.11.038 – ident: e_1_2_9_54_1 doi: 10.1002/cpe.6359 – ident: e_1_2_9_23_1 doi: 10.1016/j.csl.2004.05.007 – ident: e_1_2_9_33_1 doi: 10.1016/j.procs.2015.02.026 – ident: e_1_2_9_12_1 doi: 10.1080/03610927408827101 – ident: e_1_2_9_47_1 doi: 10.1007/3-540-36175-8_7 – ident: e_1_2_9_21_1 doi: 10.1109/ICDM.2003.1250972 – ident: e_1_2_9_46_1 doi: 10.1016/S0957-4174(02)00185-9 – ident: e_1_2_9_27_1 doi: 10.1016/j.ins.2009.02.019 – ident: e_1_2_9_28_1 doi: 10.1016/j.eswa.2014.10.023 – volume: 5 start-page: 297 year: 2021 ident: e_1_2_9_53_1 article-title: SMaRT: a framework for social media based recommender for tourism publication-title: Trans Comput Sci Comput Intell – volume: 9 start-page: 2579 year: 2008 ident: e_1_2_9_19_1 article-title: Visualizing data using t‐SNE publication-title: J Mach Learn Res – ident: e_1_2_9_26_1 doi: 10.1075/cilt.189.35deb – ident: e_1_2_9_58_1 doi: 10.1145/2766462.2767755 – volume: 98 start-page: 296 year: 1998 ident: e_1_2_9_7_1 article-title: An information‐theoretic definition of similarity publication-title: Icml – ident: e_1_2_9_6_1 – ident: e_1_2_9_24_1 doi: 10.3115/1621445.1621458 – volume: 1 start-page: 127 year: 2005 ident: e_1_2_9_22_1 article-title: Neural network based document clustering using WordNet ontologies publication-title: Int J Hybrid Intell Syst – ident: e_1_2_9_20_1 doi: 10.1126/science.295.5552.7a – ident: e_1_2_9_4_1 doi: 10.3115/981732.981751 – ident: e_1_2_9_17_1 doi: 10.1037/h0071325 – ident: e_1_2_9_31_1 doi: 10.1016/j.patcog.2017.09.045 – ident: e_1_2_9_41_1 doi: 10.1109/IJCNN.1998.685895 – volume: 5 start-page: 15 year: 2010 ident: e_1_2_9_48_1 article-title: A novel method of spam mail detection using text based clustering approach publication-title: Int J Comput Appl – ident: e_1_2_9_43_1 doi: 10.1109/RAICS.2018.8635080 – ident: e_1_2_9_29_1 doi: 10.1016/j.jocs.2013.11.007 – ident: e_1_2_9_3_1 doi: 10.1145/219717.219748 – ident: e_1_2_9_40_1 doi: 10.1016/j.neucom.2017.11.019 – ident: e_1_2_9_32_1 doi: 10.1016/S2095-3119(12)60064-1 – ident: e_1_2_9_34_1 doi: 10.1016/j.asoc.2016.01.019 – ident: e_1_2_9_35_1 doi: 10.1016/j.future.2017.12.005 – ident: e_1_2_9_42_1 doi: 10.1080/13102818.2014.949045 – ident: e_1_2_9_25_1 doi: 10.1016/j.csl.2004.05.004 – ident: e_1_2_9_38_1 doi: 10.1016/j.asoc.2014.11.015 – volume: 1245 start-page: 407 year: 2020 ident: e_1_2_9_52_1 article-title: A sentiment‐based recommender system framework for social media big data using open‐source tech stack publication-title: Adv Intell Syst Comput doi: 10.1007/978-981-15-7234-0_36 – ident: e_1_2_9_57_1 doi: 10.1145/2872427.2883037 – volume: 17 start-page: 41 year: 2005 ident: e_1_2_9_18_1 article-title: A direct formulation for sparse PCA using semidefinite programming publication-title: Adv Neural Inf Proces Syst – ident: e_1_2_9_51_1 doi: 10.1016/j.ipm.2019.102078 – volume-title: Foundations of Statistical Natural Language Processing year: 1999 ident: e_1_2_9_2_1 – ident: e_1_2_9_9_1 doi: 10.1108/eb026526 – ident: e_1_2_9_45_1 doi: 10.1007/11424918_14 – ident: e_1_2_9_39_1 doi: 10.1016/j.knosys.2014.11.028 – ident: e_1_2_9_10_1 doi: 10.1037/h0054116 |
| SSID | ssj0011031 |
| Score | 2.3018382 |
| Snippet | Social media being the most eminent source toward the growth of big data is important for information retrieval‐based applications to improve the efficiency in... |
| SourceID | proquest crossref wiley |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| SubjectTerms | Algorithms Big Data Clustering Digital media dimensionality reduction Information retrieval Reduction Segmentation social media data Social networks Taxonomy taxonomy grooming |
| Title | Taxonomy grooming algorithm ‐ An autodidactic domain specific dimensionality reduction approach for fast clustering of social media text data |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fcpe.6837 https://www.proquest.com/docview/2651502049 |
| Volume | 34 |
| WOSCitedRecordID | wos000749260800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1532-0634 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0011031 issn: 1532-0626 databaseCode: DRFUL dateStart: 20010101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEB509eDFt7i-GEH0VN2m76OoiwcRERVvJU1SXVhb2e2KR_-B_kZ_iTN9rAoKgqcemtCSmUm-JDPfB7Dj2kbZiW8sR3cMbVACaYU6ta3QRLYMHDt1SuL5m7Pg_Dy8vY0u6qxKroWp-CHGB24cGeV8zQEuk-HBJ2moejT7Pm2vJmFKkNu6LZg6vuxen43vEFjAoGJLFVaHcHtDPdsRB03f74vRJ8L8ilPLhaY7959fnIfZGl7iYeUPCzBhskWYa6QbsI7kJXi9ks9lOQPeMXSm9Qtl_y4f9Ir7B3x_ecPDDOWoyHVPl1VUqPMH2cuQCzM5uQg1qwJUjB6E43HADLBsY2xIypHQMKZyWKDqj5iNgb-Rp1gd0mNZsYKcdoKcpLoM192Tq6NTq9ZmsBQBBJqX_ITAh0qC1PakFwlNzqAT1zFGhZEgkytCXimhBUcFtpIBi-UJfu8oL9WBdFagleWZWQUMHI9J61RKyM0NtS9pE6cTu6MS8iLp-m3Ya4wUq5q4nPUz-nFFuSxiGueYx7kN2-OWjxVZxw9tNho7x3W4DmPBgvBcJhy1Ybe06K_946OLE36u_bXhOswILplgxldvA1rFYGQ2YVo9Fb3hYKt22g_d0vSH |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Na9tAEB0Sp9BemqRtiNu0mUBoT6r1_UFPIbVxqWNMsUNuYrW7SgyOFGS59Nh_0P7G_pLO6MNpIIVATjpoFwnNzO7b1b73AI5dS0sr8bXhKFPTAiUQRqhSywh1ZInAsVKnEp4_HwXjcXhxEU024FPLhan1IdYbblwZ1XjNBc4b0r1b1VB5oz_6tL7ahC2XssjrwNbnb4PZaP0TgR0MarlU2zAJuLfas6bda_venY1uIea_QLWaaQbbj3rHHXjeAEw8qTNiFzZ09gK2W_MGbGr5Jfyaih8VoQEvGTzTDIZicZkX8_LqGv_8_I0nGYpVmau5qnhUqPJrMc-QqZl8vAgV-wLUmh6E5LFgDViOMrYy5Uh4GFOxLFEuVqzHwM_IU6y36bHirCAfPEE-pvoKZoP-9HRoNO4MhiSIQCOTnxD8kEmQWp7wIltROqjEdbSWYWRT0CVhr5TwgiMDS4qA7fJsvu9IL1WBcPagk-WZ3gcMHI9l62RK2M0NlS9oGacSy5QJ5ZFw_S58aKMUy0a6nB00FnEtumzH9J1j_s5dOFq3vKnlOu5pc9AGOm4KdhnbbAnPROGoC--rkP63f3w66fP19UMbHsLT4fRsFI--jL--gWc2EyhY_9U7gE5ZrPRbeCK_l_Nl8a7J4L9y8fh3 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS9xAFD5YLcUXta3F9XqE0j5FN_cEn0RdlC7LIlp8C5O52IU1WXaz4qP_QH-jv8RzclkVKhT6lIfMkJBzzsw3k_m-D-C7Z2tpp4G2XNXWtEAJhRUpY1uRjm0RurZxS-H5392w14uuruL-HBw0XJhKH2K24caVUY7XXOB6pMz-i2qoHOm9gNZXH2DB8-OAqnLh-Lxz2Z39RGAHg0ou1bHaBNwb7dm2s9_0fTsbvUDM10C1nGk6y__1jiuwVANMPKwy4jPM6ewLLDfmDVjX8ld4uBB3JaEBrxk80wyGYnidjwfFnxt8un_EwwzFtMjVQJU8KlT5jRhkyNRMPl6Ein0BKk0PQvI4Zg1YjjI2MuVIeBiNmBQoh1PWY-Bn5AarbXosOSvIB0-Qj6muwmXn5OLo1KrdGSxJEIFGpiAl-CHT0Ni-8GNHUTqo1HO1llHsUNAlYS9DeMGVoS1FyHZ5Dt93pW9UKNxvMJ_lmV4DDF2fZeukIezmRSoQtIxTqd2WKeWR8IIW_GyilMhaupwdNIZJJbrsJPSdE_7OLdidtRxVch1_abPZBDqpC3aSOGwJz0ThuAU_ypC-2z856p_wdf1fG-7Ap_5xJ-me9X5twKLD_AmWf_U3Yb4YT_UWfJS3xWAy3q4T-BkLNvfy |
| 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=Taxonomy+grooming+algorithm+%E2%80%90+An+autodidactic+domain+specific+dimensionality+reduction+approach+for+fast+clustering+of+social+media+text+data&rft.jtitle=Concurrency+and+computation&rft.au=Renjith%2C+Shini&rft.au=Sreekumar%2C+A&rft.au=Jathavedan%2C+M&rft.date=2022-05-15&rft.pub=Wiley+Subscription+Services%2C+Inc&rft.issn=1532-0626&rft.eissn=1532-0634&rft.volume=34&rft.issue=11&rft_id=info:doi/10.1002%2Fcpe.6837&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1532-0626&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1532-0626&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1532-0626&client=summon |