https://onlineresource.ucsy.edu.mm/handle/123456789/883 Sat, 18 Jul 2026 01:44:40 GMT 2026-07-18T01:44:40Z https://onlineresource.ucsy.edu.mm/handle/123456789/1097 Informative 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. Wed, 27 Dec 2017 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/1097 2017-12-27T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/1096 Ontology 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. Wed, 27 Dec 2017 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/1096 2017-12-27T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/1095 Web 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. Wed, 27 Dec 2017 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/1095 2017-12-27T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/1094 Web 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. Wed, 27 Dec 2017 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/1094 2017-12-27T00:00:00Z