• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.45-70
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• 2012

There have been many methods and algorithms proposed for multidimensional scaling to mapping the relationships between data objects into low dimensional space. But traditional techniques, such as PROXSCAL or ALSCAL, were found not effective for visualizing the proximities between objects and the structure of clusters of large data sets have more than 50 objects. The CLUSCAL(CLUster-oriented SCALing) technique introduced in this paper differs from them especially in that it uses cluster structure of input data set. The CLUSCAL procedure was tested and evaluated on two data sets, one is 50 authors co-citation data and the other is 85 words co-occurrence data. The results can be regarded as promising the usefulness of CLUSCAL method especially in identifying clusters on MDS maps.

• Fashion & Textile Research Journal
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• v.2 no.4
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• pp.308-316
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• 2000

The purposes of this study were to identify the brand positioning of children's wear according to fashion life style and to construct brand positioning maps by using multidimensional scaling (MDS). A total of 222 responses were collected from married women aged 25 to 35 through questionnaire. Cluster analysis on fashion life style factors identified three groups: Fashion Indifference group (34%), Fashion & Individuality Oriented group (27%) and Rationality Oriented group (37%). ANOVA revealed significant differences among the three groups on the six fashion life style factors. MDS analysis showed that three segmented groups evaluated nine children's wear brand for seven attributes(color, design, price, utility, quality, brand name, fashion).

• Journal of the Korean Society of Costume
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• v.15
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• pp.253-265
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• 1990

This article was written with two purposes in mind. The first purpose was to introduce clothing and textile community who may not be familiar with Multidimensional Scaling(MDS) with usefulness of the new technique in the area of fashion merchandising. The second purpose was to present the results of an empirical study on brand image utilizing MDS and its related technique as the main analysis tools. The main objective of the empirical study was to gain a better understanding of consumer's brand image by relating differences in perception and attributes of clothing in women's ready-to wear market. For this empirical study, the ten brands and the fifteen attributes of clothing were chosen. The questionnaire consisting of questions asking about the similarity and attributes of clothing between selected brands was administrated to 185 career women during summer in 1989. Data were analyzed cluster analysis, and KYST and PROFIT in MDS program. The results were as follows: 1. The similarities data for the ten selected brand by using KYST program of MDS drawed the perceptual map. The results of this perceptual map showed that the selected brand were grouped into three clusters. 2. In order to get a somewhat objective view of which attributes consumers are attributing to each brand, PROFIT program was used. As a result, it was revealed that assortment depth / width, price, youth-oriented style, possibility of various social activity were significant attributes in consumer's brand choice rather than physical attributes of clothing such as quality or durability. This may imply that consumer orientation in rapidly changing environments of women's apparel market was its basic idea, and the focus of all fashion merchandising activities was put on need's and the response of consumer group who are the object of the target. Implicating for future research as well as for strategy of brand positioning were also suggested.

• 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.