• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.1
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• pp.73-79
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• 2009

Analyze semantic of files recorded in the user's computer file system based on C++ program language which pursue modularization program and object-oriented programming language. And this refers to it, it design that clustering semantic of filename with thesaurus for user convenience. WTPM makes User Write Files into Cluster with thesaurus semantic structure and reserved words. WTPM process has designed for Icon file's display Mashup structure and implemented by automation algorithm of classification.

• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2693-2706
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• 1999

Reengineering to object-oriented system is needed to maintain the system and satisfy requirements of structure change. Target systems which should be reengineered to object-oriented system are difficult to change because these systems have no design document or their design document is inconsistent of source code. Using design document to identifying objects for these systems is improper. There are several researches which identify objects through procedural source code analysis. In this paper, we propose automatic object identification method based on clustering of VTFG(Variable-Type-Function Graph) which represents relations among variables, types, and functions. VTFG includes relations among variables, types, and functions that may be basis of objects, and weights of these relations. By clustering related variables, types, and functions using their weights, our method overcomes limit of existing researches which identify too big objects or objects excluding many functions. The method proposed in this paper minimizes user's interaction through automatic object identification and make it easy to reenginner procedural system to object-oriented system.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.85-94
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• 2013

With the emergence of world wide web, in particular web 2.0 the rapidly growing amount of news articles has created a problem for users in selection of news articles according to their requirements. To overcome this problem different clustering mechanism has been proposed to broadly categorize news articles. However these techniques are totally machine oriented techniques and lack users' participation in the process of decision making for membership of clustering. In order to overcome the issue of zero-participation in the process of clustering news articles in this paper we have proposed a framework for clustering news articles by combining users' judgments that they post on twitter with the news articles to cluster the objects. We have employed twitter hash-tags for this purpose. Furthermore we have computed the credibility of users' based on frequency of retweets for their tweets in order to enhance the accuracy of the clustering membership function. In order to test performance of proposed methodology, we performed experiments on tweets messages tweeted during general election 2013 in Pakistan. Our results proved over claim that using users' output better outcome can be achieved then ordinary clustering algorithms.

• The Transactions of the Korea Information Processing Society
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• v.4 no.5
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• pp.1257-1266
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• 1997

The concepts of class, inheritance and information hicing and so on provide the great strengthes of object-oriented languages, but they also introduce diffculties in porfram analysis and understanding. Particulary, it is move difficult to umderstand the dyamic aspects than the static ones of object-oriented programs. The dyamicaspects can be understood by recognizing the event's reciprocal action among the classes. In this paper, it will be supplied to the reprecentation of event abstraction which is useful for understanding the object-oriented programs.And the clustering concept with the events will be applied to abstract the events. By clustering the events, user can get the information about function of the classes and the reteival of the class library.

• Journal of the Korea Society of Computer and Information
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• v.7 no.4
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• pp.1-7
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• 2002

In this paper, it will be supplied to the representation of event abstraction which is useful for understanding information relationship of the object-oriented programs. And the clustering concept with the events will be applied to abstract the events. By clustering the events, user can set the information about the function of the classes and the retrieval of the class library.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.3
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• pp.55-64
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• 2014

While utilizing high resolution satellite image for land use classification has been popularized, object-oriented classification has been adapted as an affordable classification method rather than conventional statistical classification. The aim of this study is to extract the paddy field area using object-oriented classification with time series NDVI from high-resolution satellite images, and the RapidEye satellite images of Jaeryung-gun in North Korea were used. For the implementation of object-oriented classification, creating objects by setting of scale and color factors was conducted, then 3 different land use categories including paddy field, forest and water bodies were extracted from the objects applying the variation of time-series NDVI. The unclassified objects which were not involved into the previous extraction classified into 6 categories using unsupervised classification by clustering analysis. Finally, the unsuitable paddy field area were assorted from the topographic factors such as elevation and slope. As the results, about 33.6 % of the total area (32313.1 ha) were classified to the paddy field (10847.9 ha) and 851.0 ha was classified to the unsuitable paddy field based on the topographic factors. The user accuracy of paddy field classification was calculated to 83.3 %, and among those, about 60.0 % of total paddy fields were classified from the time-series NDVI before the unsupervised classification. Other land covers were classified as to upland(5255.2 ha), forest (10961.0 ha), residential area and bare land (3309.6 ha), and lake and river (1784.4 ha) from this object-oriented classification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5849-5855
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• 2011

As application development environment changes rapidly, importance of user interface design is increasing. Usually, most of designers are clustering by subjective method of individual to define objects that have relativity in design interface. But, interface which is designed without particular rules just adds inefficiency and complexity of business to user who use this system. Therefore, in this paper, we propose an object oriented design model that allows for flexible development by formalizing the user interface prototype in any GUI environment. The visual cohesion of the user interface is a new set of criteria which has been studied in relation to the user interface contents, and is founded on the basis of the cohesion of the interface as defined using basic software engineering concepts. The visual cohesion includes the issue of how each unit is arranged and grouped, as well as the cohesion of the business events which appear in the programming unit. The interface will become easier to understand and use if the business events are grouped by their inter-relevance within the user interface.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1984-2002
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• 2020

With the appearance of wireless spectrum crisis in traditional cellular network, device-to-device (D2D) communication has been regarded as a promising solution to ease heavy traffic burden by enabling precise content delivery among mobile users. However, due to the channel sharing, the interference between D2D and cellular users can affect the transmission rate and narrow the throughput in the network. In this paper, we firstly present a weighted interference minimization cluster formation model involving both social attribute and physical closeness. The weighted-interference, which is evaluated under the susceptible-infected(SI) model, is utilized to gather user in social and physical proximity. Then, we address the cluster formation problem via spectrum clustering with iterative operation. Finally, we propose the stable matching theory algorithm in order to maximize rate oriented to accomplish the one-to-one resource allocation. Numerical results show that our proposed scheme acquires quite well clustering effect and increases the accumulative transmission rate compared with the other two advanced schemes.

• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.83-99
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• 2007

Nowadays, the advancement of digital information technology resulting in the increased interest of the management and the use of information has given stimulus to the research on the use and management of information. In this paper, we propose an integrated data mining model that can provide the necessary information and interface to users of scientific information portal service according to their respective classification groups. The integrated model classifies users from log files automatically collected by the web server based on users' behavioral patterns. By classifying the existing users of the web site, which provides information service, and analyzing their patterns, we proposed a web site utilization methodology that provides dynamic interface and user oriented site operating policy. In addition, we believe that our research can provide continuous web site user support, as well as provide information service according to user classification groups.