• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.17-24
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• 2011

Collaborative filtering system is the clustering about user is built and then based on that clustering results will recommend the preferred item to the user. However, building user clustering is time consuming and also once the users evaluate and give feedback about the film then rebuilding the system is not simple. In this paper, genre pattern of movie recommendation systems is being used and in order to simplify and reduce time of rebuilding user clustering. A Frequent pattern networks is used and then extracts user preference genre patterns and through that extracted patterns user clustering will be built. Through built the clustering for all neighboring users to collaborative filtering is applied and then recommends movies to the user. When receiving user information feedback, traditional collaborative filtering is to rebuild the clustering for all neighbouring users to research and do the clustering. However by using frequent pattern Networks, through user clustering based on genre pattern, collaborative filtering is applied and when rebuilding user clustering inquiry limited by search time can be reduced. After receiving user information feedback through proposed user clustering based on genre pattern, the time that need to spent on re-establishing user clustering can be reduced and also enable the possibility of traditional collaborative filtering systems and recommendation of a similar performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1363-1376
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• 2020

An improved clustering scheme based on user group is proposed. Every two users are grouped among N-users in the allowed system according to their link gain from large to small. Each user group is numbered sequentially. Two user clusters are obtained according to the principle of maximizing link gain difference for the users in the first and last user groups. The remaining user groups are added to the two existing user clusters according to the parity of the group number. The clustering should be clustered again among the users in either user cluster if the throughput summation of a user cluster in NOMA is less than that of these users in orthogonal multiple access. The simulation results show that the proposed clustering scheme can increase the system throughput by about 8% compared with the hybrid clustering scheme when the number of users requiring service is 12.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.942-948
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• 2020

In this paper, we propose a clustered algorithm that possible more efficient user distinction within clustering using context-aware attribute information. In typically, the data provided to classify interrelationships within cluster information in the process of clustering data will be as a degrade factor if new or newly processing information is treated as contaminated information in comparative information. In this paper, we have developed a clustering algorithm that can extract user's recognition information to solve this problem in using K-means algorithm. The proposed algorithm analyzes the user's clustering attributed parameters from user clusters using accumulated information and clustering according to their attributes. The results of the simulation with the proposed algorithm showed that the user management system was more adaptable in terms of classifying and maintaining multiple users in clusters.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.118-126
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• 2003

In data mining, having access to large amount of data sets for the purpose of predictive data does not guarantee good method, even where the size of Real data is Mobile commerce unlimited. In addition to searching expected Goods objects for Users, it becomes necessary to develop a recommendation service based on XML. In this paper, we design the optimized XML Recommender product data. Efficient XML data preprocessing is required, include of formatting, structural, and attribute representation with dependent on User Profile Information. Our goal is to find a relationship among user interested products from E-Commerce and M-Commerce to XDB. Firstly, analyzing user profiles information. In the result creating clusters with analyzed user profile such as with set of sex, age, job. Secondly, it is clustering XML data which are associative products classify from user profile in shopping mall. Thirdly, after composing categories and goods data in which associative objects exist from the first clustering, it represent categories and goods in shopping mall and optimized clustering XML data which are personalized products. The proposed personalized user profile clustering method has been designed and simulated to demonstrate it's efficient.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Korea Artificial Intelligence Association
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• v.2 no.1
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• pp.1-6
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• 2024

In this paper, we develop an AI-based recommendation system that matches the specifications of smartphones from company 'S'. The system aims to simplify the complex decision-making process of consumers and guide them to choose the smartphone that best suits their daily needs. The recommendation system analyzes five specifications of smartphones (price, battery capacity, weight, camera quality, capacity) to help users make informed decisions without searching for extensive information. This approach not only saves time but also improves user satisfaction by ensuring that the selected smartphone closely matches the user's lifestyle and needs. The system utilizes unsupervised learning, i.e. clustering (K-MEANS, DBSCAN, Hierarchical Clustering), and provides personalized recommendations by evaluating them with silhouette scores, ensuring accurate and reliable grouping of similar smartphone models. By leveraging advanced data analysis techniques, the system can identify subtle patterns and preferences that might not be immediately apparent to consumers, enhancing the overall user experience. The ultimate goal of this AI recommendation system is to simplify the smartphone selection process, making it more accessible and user-friendly for all consumers. This paper discusses the data collection, preprocessing, development, implementation, and potential impact of the system using Pandas, crawling, scikit-learn, etc., and highlights the benefits of helping consumers explore the various options available and confidently choose the smartphone that best suits their daily lives.

• Genomics & Informatics
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• v.10 no.4
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• pp.263-265
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• 2012

We developed a user-friendly, interactive program to simultaneously cluster and visualize omics data, such as DNA and protein array profiles. This program provides diverse algorithms for the hierarchical clustering of two-dimensional data. The clustering results can be interactively visualized and optimized on a heatmap. The present tool does not require any prior knowledge of scripting languages to carry out the data clustering and visualization. Furthermore, the heatmaps allow the selective display of data points satisfying user-defined criteria. For example, a clustered heatmap of experimental values can be differentially visualized based on statistical values, such as p-values. Including diverse menu-based display options, QCanvas provides a convenient graphical user interface for pattern analysis and visualization with high-quality graphics.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.986-1016
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• 2019

Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.

• Journal of the Korea Society of Computer and Information
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• v.13 no.4
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• pp.11-18
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• 2008

Document clustering is an important method for document analysis and is used in many different information retrieval applications. This paper proposes a new document clustering model using the re-weighted term based NMF(non-negative matrix factorization) to cluster documents relevant to a user's requirement. The proposed model uses the re-weighted term by using user feedback to reduce the gap between the user's requirement for document classification and the document clusters by means of machine. The Proposed method can improve the quality of document clustering because the re-weighted terms. the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set more well. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than documents clustering methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.114-121
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• 2022

In this paper, we consider a user centric clustering in order to guarantee the performance of the users in cell free multiple-input multiple-output (MIMO) network. In the user centric clustering scheme, by using large scale fading coefficients of the connected access points (APs), each user decides own cluster with the APs having the higher the large scale fading coefficients than threshold value compared to the highest large scale fading coefficient. In the determined user centric clusters, the APs design the beamformers and power allocations in the distributed manner and the APs cooperatively transmit data to users by using beamformers and power allocations. In the simulation results, we verify the performance of user centric clustering in terms of the spectral efficiency and we also find the optimal threshold value in the given configuration.