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": "e5426361-a2e8-4074-9bef-76520aee87a3"}, "_deposit": {"id": "4226", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4226"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4226", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Clustering XML Document Based On Path Similarities Using Structure Only", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "We propose a methodology for clustering XMLdocuments on the basis of their structuralsimilarities. This research combines the methods ofcommon XPath and K-means clustering that improvethe efficiency for those XML documents with manydifferent structures. The common XPath is used forsearching similarities between huge numbers of XMLdocuments’ paths. K-means clustering algorithm isessentially used to accurate clusters. In order tocluster the documents’ paths we indicate the steps bystep methods. The first step includes frequentstructure mining for searching similarities betweenthe huge amounts of XML documents’ structures byusing the F-P growth method. The second step buildsdimensional feature vector matrix by using extractedpaths. Based on the set of common path vectorscollected, we compute the structure similaritybetween the XML documents. And the last steputilizes the K-means clustering algorithm is used tocreate accurate clusters which are based on the ideaof using path based clustering, which groups thedocuments according to their common XPaths, i.e.their frequent structures. The quality of clusteringcan be measured on the dissimilarity of documentstructures. Also, experimental evaluation performedon both synthetic and real data shows theeffectiveness of our approach."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "common XPath"}, {"interim": "K-means clustering"}, {"interim": "XML Document Clustering"}, {"interim": "Data Mining"}, {"interim": "Frequent Structure Mining"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-08-06"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "59027.pdf", "filesize": [{"value": "100 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 100000.0, "url": {"url": "https://meral.edu.mm/record/4226/files/59027.pdf"}, "version_id": "dd00b9b2-e941-46b6-a01d-00a691fa2028"}]}, "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": "Mon, Ei Ei"}, {"subitem_authors_fullname": "Tun, Khin Nwe Ni"}]}]}, "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/123456789/1918"}, "item_title": "Clustering XML Document Based On Path Similarities Using Structure Only", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004226", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-08-06"}, "publish_date": "2019-08-06", "publish_status": "0", "recid": "4226", "relation": {}, "relation_version_is_last": true, "title": ["Clustering XML Document Based On Path Similarities Using Structure Only"], "weko_shared_id": -1}
  1. University of Computer Studies, Yangon
  2. Conferences

Clustering XML Document Based On Path Similarities Using Structure Only

http://hdl.handle.net/20.500.12678/0000004226
http://hdl.handle.net/20.500.12678/0000004226
86814ee8-e2e9-4bf2-904a-3d013a5d0793
e5426361-a2e8-4074-9bef-76520aee87a3
None
Preview
Name / File License Actions
59027.pdf 59027.pdf (100 Kb)
Publication type
Article
Upload type
Publication
Title
Title Clustering XML Document Based On Path Similarities Using Structure Only
Language en
Publication date 2009-12-30
Authors
Mon, Ei Ei
Tun, Khin Nwe Ni
Description
We propose a methodology for clustering XMLdocuments on the basis of their structuralsimilarities. This research combines the methods ofcommon XPath and K-means clustering that improvethe efficiency for those XML documents with manydifferent structures. The common XPath is used forsearching similarities between huge numbers of XMLdocuments’ paths. K-means clustering algorithm isessentially used to accurate clusters. In order tocluster the documents’ paths we indicate the steps bystep methods. The first step includes frequentstructure mining for searching similarities betweenthe huge amounts of XML documents’ structures byusing the F-P growth method. The second step buildsdimensional feature vector matrix by using extractedpaths. Based on the set of common path vectorscollected, we compute the structure similaritybetween the XML documents. And the last steputilizes the K-means clustering algorithm is used tocreate accurate clusters which are based on the ideaof using path based clustering, which groups thedocuments according to their common XPaths, i.e.their frequent structures. The quality of clusteringcan be measured on the dissimilarity of documentstructures. Also, experimental evaluation performedon both synthetic and real data shows theeffectiveness of our approach.
Keywords
common XPath, K-means clustering, XML Document Clustering, Data Mining, Frequent Structure Mining
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1918
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
Back
0
0
views
downloads
See details
Views Downloads

Versions

Ver.1 2020-09-01 14:22:10.261823
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