{"id":160572,"date":"2011-04-28T00:00:00","date_gmt":"2011-04-28T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/efficient-document-clustering-via-online-nonnegative-matrix-factorizations\/"},"modified":"2018-10-16T20:22:23","modified_gmt":"2018-10-17T03:22:23","slug":"efficient-document-clustering-via-online-nonnegative-matrix-factorizations","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/efficient-document-clustering-via-online-nonnegative-matrix-factorizations\/","title":{"rendered":"Efficient Document Clustering via Online Nonnegative Matrix Factorizations"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In recent years, Nonnegative Matrix Factorization (NMF) has received considerable interest from the data mining and information retrieval fields. NMF has been successfully applied in document clustering, image representation, and other domains. This study proposes an online NMF (ONMF) algorithm to efficiently handle very large-scale and\/or streaming datasets. Unlike conventional NMF solutions which require the entire data matrix to reside in the memory, our ONMF algorithm proceeds with one data point or one chunk of data points at a time. Experiments with one-pass and multi-pass ONMF on real datasets are presented.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In recent years, Nonnegative Matrix Factorization (NMF) has received considerable interest from the data mining and information retrieval fields. NMF has been successfully applied in document clustering, image representation, and other domains. This study proposes an online NMF (ONMF) algorithm to efficiently handle very large-scale and\/or streaming datasets. Unlike conventional NMF solutions which require the [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Society for Industrial and Applied Mathematics","msr_publisher_other":"","msr_booktitle":"Eleventh SIAM International Conference on Data Mining","msr_chapter":"","msr_edition":"Eleventh SIAM International Conference on Data Mining","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Eleventh SIAM International Conference on Data Mining","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Fei Wang, Ping Li, Arnd Christian K\u00f6nig, Chenhao Tan","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2011-04-28","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2011,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13555],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-160572","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"Society for Industrial and Applied Mathematics","msr_edition":"Eleventh SIAM International Conference on Data Mining","msr_affiliation":"","msr_published_date":"2011-04-28","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"Eleventh SIAM International Conference on Data Mining","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"206597","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"FODAVA-11-02.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/FODAVA-11-02.pdf","id":206597,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":206597,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/FODAVA-11-02.pdf"}],"msr-author-ordering":[{"type":"text","value":"Fei Wang","user_id":0,"rest_url":false},{"type":"text","value":"Chenhao Tan","user_id":0,"rest_url":false},{"type":"user_nicename","value":"chrisko","user_id":31427,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=chrisko"},{"type":"text","value":"Ping Li","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[957177],"msr_project":[169514],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":169514,"post_title":"Data Exploration","post_name":"data-exploration","post_type":"msr-project","post_date":"2004-06-08 15:56:40","post_modified":"2017-06-06 10:57:58","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/data-exploration\/","post_excerpt":"This is a project area rather than a specific project. This project area focuses on novel ways to query, browse, extract, explore, mine and manage various kinds of data residing within the enterprise and on the web: structured data in relational databases, tabular data embedded in web pages, enterprise documents and spreadsheets as well as unstructured data in query logs, text documents and social media. 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