Eighth Local Conference on Parallel and Soft Computinghttps://onlineresource.ucsy.edu.mm/handle/123456789/8832024-03-28T16:14:32Z2024-03-28T16:14:32ZInformative Content Extraction for Web Page using Text Density and Visionbased Page Segmentation (VIPS) Algorithm IntegrationMon, Ei Phyu PhyuYuzanahttps://onlineresource.ucsy.edu.mm/handle/123456789/10972019-12-02T05:14:21Z2017-12-27T00:00:00ZInformative Content Extraction for Web Page using Text Density and Visionbased Page Segmentation (VIPS) Algorithm Integration
Mon, Ei Phyu Phyu; Yuzana
Web pages consist of not only actual
content, but also other elements such as branding
banners, navigational elements, advertisements,
copyright etc.Irrelevant content in the Web page is
treated as noisy content. This noisy content is
typically not related to the main subjects of the
webpages. A method is necessary to extract the
informative content and discard the noisy content
from Web pages. This system is used an integration
of textual and visual importance features to extract
the informative contents from Web pages. Initially a
web page is converted into Document Object Model
(DOM) tree. For each node in the DOM tree,
textual and visual importance is calculated. Textual
importance and visual importance is combined to
form hybriddensity.DensitySumis calculated and
used in content extraction algorithm to extract the
informative content from Web pages. The algorithm
is tested with various web domains and styles of
web pages. Performance of web content extraction
is obtained by calculating precision and recall.
2017-12-27T00:00:00ZOntology based Recommender System using Content-based Filtering and AHP methodsMon, Shun EiKham, Nang Saing Moonhttps://onlineresource.ucsy.edu.mm/handle/123456789/10962019-12-02T05:14:21Z2017-12-27T00:00:00ZOntology based Recommender System using Content-based Filtering and AHP methods
Mon, Shun Ei; Kham, Nang Saing Moon
Recommender systems on web pages are a
subclass of information filtering system that seeks
the relevant web pages according to prediction of
the 'rating' or 'preference' that a user would give
web page. Nowadays, the huge numbers of
available web pages on the web make difficultly
for finding relevant web pages. Ontology is a
formal representation of a set of concepts within a
domain and the relationships between those
concepts. Content-based filtering also referred to
as cognitive filtering, recommends items based on
a comparison between the content of the items
(Item profile) and a user profile. Rather than
prescribing a "correct" decision, the Analytic
Hierarchy Process AHP helps decision makers
find one that best solution of their goal and their
understanding of the problem and extract decision
based on their preferences. This system presents
building ontology of cosmetics to apply
recommender system and gives the best solution of
cosmetic web pages which are suitable for user
based on user’s preference using Content-based
Filtering method and Analytic Hierarchy Process
(AHP) method.
2017-12-27T00:00:00ZWeb Searching Based on Clustering ApproachSoe, ThinzarNwe, Tin Htarhttps://onlineresource.ucsy.edu.mm/handle/123456789/10952019-12-02T05:14:21Z2017-12-27T00:00:00ZWeb Searching Based on Clustering Approach
Soe, Thinzar; Nwe, Tin Htar
The dynamic web has increased
exponentially over the past few years with more
than thousands of documents related to a subject
available to user now. Most of the web documents
are unstructured and not in organized manner and
hence user facing more difficult to find relevant
documents. A more useful and efficient mechanism
is combining clustering with ranking, where
clustering can group the similar documents in one
place and ranking can be applied to each cluster
for viewing the top document at the beginning.
This paper is proposed tf-idf based MLTransTrie
(Multiple level Association Rule, Transposed
Database,Trie) algorithm for clustering the web
document. We then ranked the documents in each
cluster using tf-idf and similarity factor of
documents based on the user query. This approach
will help the user to get all his relevant document
in one place.
2017-12-27T00:00:00ZWeb Usage Mining Using Clustering and Association Rule MiningThwin, Aye TheingiKham, Nang Saing Moonhttps://onlineresource.ucsy.edu.mm/handle/123456789/10942019-12-02T05:14:21Z2017-12-27T00:00:00ZWeb Usage Mining Using Clustering and Association Rule Mining
Thwin, Aye Theingi; Kham, Nang Saing Moon
Data mining methods are used to discover
the behaviour of the users. Therefore, the data
used for the mining purpose must be qualified for
the data cleaning stage and must be considered
and planned efficiently to meet the requirement.
For this reason, the data cleaning of the preprocessing
stage becomes the essential key.
Similarity measurement method is used to discover
web usage data that have same category or usage
purpose for clustering. Association rule mining
uses the clustered data to generate rules that
discover the patterns of interest.
This proposed system presents web usage mining
using data mining methods. The main components
that are included in this system are the
preprocessing of web access log, computing
similarity measurement using Jaccard coefficient,
clustering the web pages using K-Mean Algorithm
and finally the generation of rules for frequent
pattern of web pages using Apriori Algorithm for
interesting relationships among web pages in
given web usage data set.
2017-12-27T00:00:00Z