Log in
Language:

MERAL Myanmar Education Research and Learning Portal

  • Top
  • Universities
  • Ranking
To
lat lon distance
To

Field does not validate



Index Link

Index Tree

Please input email address.

WEKO

One fine body…

WEKO

One fine body…

Item

{"_buckets": {"deposit": "e6191f27-537a-485c-aa05-456aa074865c"}, "_deposit": {"id": "4065", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4065"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4065", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Comparison of the K-Means and the K-Medoids Partitioning Algorithms for Clustering of Optical Mineralogy", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. The dissimilarity (or similarity) between the objects described by interval-scaled variables is typically computed based on the distance between each pair of objects. Euclidean distance is used in this system. K-Means and k-Medoids algorithms are by far the most widely used method for discovering clusters in data. This system can implement the clustering of optical mineralogy by using the two partitioning methods (k-Means algorithm and k-Medoids algorithm) and then evaluate the performance of the processing time, squared-error rate and average squared-error value of these algorithms. Experiments show that these two algorithms are effective for 200 records with fourteen attributes of optical mineralogy datasets and becomes more and more effective as the number of clusters increases."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value": []}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-08-05"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "55166.pdf", "filesize": [{"value": "455 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 455000.0, "url": {"url": "https://meral.edu.mm/record/4065/files/55166.pdf"}, "version_id": "0352e56c-13ba-4a13-98ad-b54c7eb220b1"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Fourth Local Conference on Parallel and Soft Computing", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Thu, Aye Nyein"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2009-12-30"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/1771"}, "item_title": "Comparison of the K-Means and the K-Medoids Partitioning Algorithms for Clustering of Optical Mineralogy", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004065", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-08-05"}, "publish_date": "2019-08-05", "publish_status": "0", "recid": "4065", "relation": {}, "relation_version_is_last": true, "title": ["Comparison of the K-Means and the K-Medoids Partitioning Algorithms for Clustering of Optical Mineralogy"], "weko_shared_id": -1}
  1. University of Computer Studies, Yangon
  2. Conferences

Comparison of the K-Means and the K-Medoids Partitioning Algorithms for Clustering of Optical Mineralogy

http://hdl.handle.net/20.500.12678/0000004065
http://hdl.handle.net/20.500.12678/0000004065
19c4a567-fa7e-4179-b833-f3b190467acd
e6191f27-537a-485c-aa05-456aa074865c
None
Preview
Name / File License Actions
55166.pdf 55166.pdf (455 Kb)
Back
0
0
views
downloads
See details
Views Downloads

Versions

Ver.1 2020-09-01 14:02:11.942032
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Export

OAI-PMH
  • OAI-PMH DublinCore
Other Formats
  • JSON

Confirm


Back to MERAL


Back to MERAL