{"_buckets": {"deposit": "defbe439-ec22-4acc-92b1-25806b995b92"}, "_deposit": {"created_by": 45, "id": "6296", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "6296"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/6296", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Performance Analysis of Parallel Clustering on Spark Computing Platform", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "In the area of information and technology, data is\ngenerated from a plethora of sources such as social\nmedia, internet of things, multimedia, sensor networks.\nClustering is an essential data mining tool for analyzing\nthis valuable information.Clustering algorithms are\ngenerally classified as a hierarchical and partitioning\nalgorithm. This paper interested in partitioning\nalgorithms. There are two kinds of partitioning algorithm,\nmean-based and medoids-based. The paper focuses on\nmedoids-based because of medoids less influence by\noutliers or other extreme values than mean. But, one of\nthe main issues of partitioning algorithm cannot handle\nlarge volume of data in case of the poor cluster quality\nand higher execution time.The objective of theresearchis\nto solve these two issues.To improve clustering quality,\nthis paper appliesswarm intelligence optimization\nalgorithm on the partition clustering algorithm. And then,\nthis paper expects to reduce execution time for clustering\nlarge volume of data by using Spark framework."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Clustering"}, {"interim": "Partitioning algorithm"}, {"interim": "Bat algorithm"}, {"interim": "Apache Spark"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-11-19"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Performance Analysis of Parallel Clustering on Spark Computing Platform.pdf", "filesize": [{"value": "1.2 Mb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensefree": "© 2018 ICAIT", "licensetype": "license_free", "mimetype": "application/pdf", "size": 1200000.0, "url": {"url": "https://meral.edu.mm/record/6296/files/Performance Analysis of Parallel Clustering on Spark Computing Platform.pdf"}, "version_id": "3571fecd-3a91-4940-a5a7-4d0bf69ea3a3"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICAIT-2018", "subitem_c_date": "1-2 November, 2018", "subitem_conference_title": "2nd International Conference on Advanced Information Technologies", "subitem_place": "Yangon, Myanmar", "subitem_session": "Data Mining", "subitem_website": "https://www.uit.edu.mm/icait-2018/"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Nway Yu Aung"}, {"subitem_authors_fullname": "Aye Chan Mon"}, {"subitem_authors_fullname": "Swe Zin Hlaing"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2018-11-02"}, "item_title": "Performance Analysis of Parallel Clustering on Spark Computing Platform", "item_type_id": "21", "owner": "45", "path": ["1605779935331"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000006296", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-11-19"}, "publish_date": "2020-11-19", "publish_status": "0", "recid": "6296", "relation": {}, "relation_version_is_last": true, "title": ["Performance Analysis of Parallel Clustering on Spark Computing Platform"], "weko_shared_id": -1}