https://onlineresource.ucsy.edu.mm/handle/123456789/29 Mon, 08 Jun 2026 10:28:48 GMT 2026-06-08T10:28:48Z https://onlineresource.ucsy.edu.mm/handle/123456789/2602 Topic Extraction of Crawled Documents Collection using Correlated Topic Model in MapReduce Framework Oo, Mi Khine; Khine, May Aye The tremendous increase in the amount of available research documents impels researchers to propose topic models to extract the latent semantic themes of a documents collection. However, how to extract the hidden topics of the documents collection has become a crucial task for many topic model applications. Moreover, conventional topic modeling approaches suffer from the scalability problem when the size of documents collection increases. In this paper, the Correlated Topic Model with variational Expectation Maximization algorithm is implemented in MapReduce framework to solve the scalability problem. The proposed approach utilizes the dataset crawled from the public digital library. In addition, the full-texts of the crawled documents are analysed to enhance the accuracy of MapReduce CTM. The experiments are conducted to demonstrate the performance of the proposed algorithm. From the evaluation, the proposed approach has a comparable performance in terms of topic coherences with LDA implemented in MapReduce framework. Topic Extraction of Crawled Documents Collection using Correlated Topic Model in MapReduce Framework Sun, 01 Dec 2019 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/2602 2019-12-01T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/2601 Correlated Topic Modeling for Big Data with MapReduce Oo, Mi Khine; Khine, May Aye Efficient extraction of useful information is a rising problem in Big data, since the amount of information being gathered across various domains grows with an increasing rate. So, it takes more time to understand the underlying themes of the documents collection. To deal with such problem in the context of Big data, the proposed approach implements the correlated topic model (CTM) with MapReduce framework to reveal the thematic information represented by words, to speed up the processing and to increase the scalability of the model. We apply variational Expectation-Maximization (EM) to make inference for CTM. In this paper, academic articles are collected by using a web crawler. Then CTM is exploited to uncover the underlying themes of the collection. The use of CTM with MapReduce implementation improves the accuracy and performance in a reliable and scalable manner. Correlated Topic Modeling for Big Data with MapReduce Mon, 01 Oct 2018 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/2601 2018-10-01T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/2600 Science-related Articles Recommendation System from Big Data Oo, Mi Khine; Khaing, Myo Kay Under the explosive increase of global data, the term Big data is mainly used to describe enormous datasets. With the availability of increasingly large quantities of digital information, it is becoming more difficult for researchers to extract and find relevant articles pertinent to their interests. In this system, we propose an approach to discover and recommend the desired articles by combining collaborative filtering (CF) with topic modeling. Correlated Topic Model (CTM) is used for modeling topics. Our approach not only considers the interactions between users through collaborative filtering but also learns the properties of items involved through topic modeling to improve recommendation. In order to handle a large dataset, a Big data analytics tool Hadoop is used to perform processing over distributed clusters. The proposed approach learns the accuracy of the recommendation. Science-related Articles Recommendation System from Big Data Mon, 01 Feb 2016 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/2600 2016-02-01T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/2507 A Study on Community Overlapping Detection Algorithms in Social Networks Win, Eaint Mon; Khine, May Aye Community detection is one of the most important research area wherein invention and growth of social networks. Community is a set of members densely connected within a group and sparely connected with the other groups. In social networks, the singular characteristic of communities is multi membership of a node resulting in overlapping communities. Another relevant feature of social networks is the possibility to evolve over time. In recent years, many researchers have worked on various methods that can efficiently unveil overlapped structure on dynamic network. This paper reviews the previous studies done on the problem of overlapping community detection algorithms. Moreover, some approaches for dynamic network that change from time to time are also described. Fri, 28 Feb 2020 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/2507 2020-02-28T00:00:00Z