• Journal of KIISE:Databases
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• v.29 no.1
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• pp.1-14
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• 2002

Association rule discovery is a method of mining for the associated item set on large databases based on support and confidence threshold. The discovered association rules can be applied to the marketing pattern analysis in E-commerce, large shopping mall and so on. The association rule discovery makes multiple scan over the database storing large transaction data, thus, the algorithm requiring very high overhead might not be useful in real-time association rule discovery in dynamic environment. Therefore this paper proposes an active candidate set management model based on trigger and incremental update mechanism to overcome non-realtime limitation of association rule discovery. In order to implement the proposed model, we not only describe an implementation model for incremental updating operation, but also evaluate the performance characteristics of this model through the experiment.

• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.121-128
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• 2010

Spatial data mining has been developed to support spatial association knowledge between spatial features or its non-spatial attributes for an application areas. At the present time, a number of researchers attempt to the data mining techniques apply to the several analysis areas, for examples, civil engineering, environmental, agricultural areas. Despite the efforts that, until such time as not existed practical systems for the gCRMDMs. gCRMDMs is merged with very large spatial database and CRM information system. Also, it is discovery the association rule for the predictions of customer's shopping pattern informations in a huge database consisted with spatial and non-spatial dataset. For this goal, gCRMDMs need spatial data mining techniques. But, nowadays, in a most case not exist utilizable model for the gCRMDMs. Therefore, in this paper, we proposed a practical gCRMDMs model to support a customer, store, street, building and geographical suited to the trade area.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.185-190
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• 2006

Most systems in ubiquitous computing analyze context information of users which have similar propensity with demographics methods and collaborative filtering to provide personalized recommendation services. The systems have mostly used static context information such as sex, age, job, and purchase history. However the systems have limitation to analyze users' propensity accurately and to provide personalized recommendation services in real-time, because they have difficulty in considering users situation as moving path. In this paper we use users' moving path of dynamic context to consider users situation. For the prediction accuracy we complete with a path completion algorithm to moving path which is inputted to RSOM. We train the moving path to be completed by RSOM, analyze users' moving pattern and predict a future moving path. Then we recommend the nearest product on the prediction path with users' high preference in real-time. As the experimental result, MAE is lower than 0.5 averagely and we confirmed our method can predict users moving path correctly.

• Journal of Environmental Health Sciences
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• v.33 no.1 s.94
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• pp.49-56
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• 2007

The purpose of this study was to survey the distribution patterns of volatile organic compounds(VOCs) and formaldehyde in the various indoor environments using cluster analysis. We investigated VOCs and formaldehyde in subway stations, underground shopping areas, medical centers, maternity recuperation centers, public childcare centers, large stores, funeral houses, and indoor parking lots from June,2005 to May,2006. Concentration of TVOCs in maternity recuperations was 2,605.7 ${\mu}g/m^3$ that was higher than the guideline and other facilities. TVOCs in public childcare centers was 1,951.6 ${\mu}g/m^3$ also it exceeded the guideline. Moreover, concentration of TVOCs in every facility exceeded the guideline of Department of Environment, Korea. In case of formaldehyde, mean concentration, 336.5 ${\mu}g/m^3$, in only public childcare centers exceeded the 120 ${\mu}g/m^3$ of the guideline. Finally, by applying cluster analysis, three pattterns of the indoor air pollutions were distinguished. In the results of analysis, concentrations of TVOCs and formaldehyde of cluster 3 were higher than cluster 1 and 2 that were 2,561.4 ${\mu}g/m^3$ and 184.9 ${\mu}g/m^3$, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.373-378
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• 2017

While on-line shopping markets are growing, consumer's procurement processes are being confused regardless of on or off line market and, smart consumers who want intelligent tailored services have emerged. Depending on the changeable pattern of consumer, most of related companies provide various Omni channel and O2O service. However, reactions of the fashion companies are tend to be late. Recently, the IoT environment has changed to standards-based open platform and it requires a variety of intelligent services depending on the type of environment and objects. This thesis proposes fashion O2O system using smart fitting display that is adaptable to fashion companies. This proposed system provides fitting information which is performed on off-line by users after constructing the database, it also support the works as on-line status, thus, it makes users' procurements to maintain continuously. For the more, customer oriented intelligent fitting service would be expected by the information connection with the shop and delivery systems.

• The KIPS Transactions:PartC
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• v.9C no.2
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• pp.181-188
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• 2002

There are recently a lot of inconvenience because every client should be set to the proxy server on the browser in order to control the access by means of the client authentication in the proxy server. The client authentication technology using the transparency cache in this paper will be transparently used for every user in the internet which option of the authentication function is simply set in the cache server. In addition, the administrator will get the benefit since he can control the traffic of each client and strengthen the security. And also, this system is expected to use in the eCRM deeply rotated to the tendency of the client in the field of the e-commerce like shopping mall in the internet since the administrator can monitor the pattern of the client using the internet. This technique can be applied to the company affiliated research center, the EC website, and the military where it is essential for the tight security even though there are no additional security devices.

• Journal of Environmental Health Sciences
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• v.45 no.2
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• pp.164-172
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• 2019

Objectives: This study's purpose is finding children's activity spaces that demand environmental safety management. Methods: The method of this study is analysing children's life patterns based on a questionnaire survey. Results: This study analyzed children's life patterns through a questionnaire survey. In total, 2,447 questionnaires were provided to analyze children's life patterns. The results of the questionnaire indicated a highly simple form because many children generally stayed in their home (66%) or nursery facility (2%). In the case of other facilities, playground was ranked first and amusement park was ranked second. In addition, kids cafe (including play facilities installed in shopping centers, etc.), library, and internet cafe were among the responses. Conclusions: The priority for new high-maintenance children's activity spaces are academy (rank 1), kids cafe (rank 2), indoor playground (rank 3).

• The Journal of Bigdata
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• v.5 no.2
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• pp.135-143
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• 2020

This paper proposes a pre-processing method and a dimensional reduction method in the analysis of shopping carts where there are many correlations between variables when dividing the types of consumers in the agri-food consumer panel data. Cluster analysis is a widely used method for dividing observational objects into several clusters in multivariate data. However, cluster analysis through dimensional reduction may be more effective when several variables are related. In this paper, the food consumption data surveyed of 1,987 households was clustered using the K-means method, and 17 variables were re-selected to divide it into the clusters. Principal component analysis and factor analysis were compared as the solution for multicollinearity problems and as the way to reduce dimensions for clustering. In this study, both principal component analysis and factor analysis reduced the dataset into two dimensions. Although the principal component analysis divided the dataset into three clusters, it did not seem that the difference among the characteristics of the cluster appeared well. However, the characteristics of the clusters in the consumption pattern were well distinguished under the factor analysis method.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.193-200
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• 2014

Due to the advent of ubiquitous computing environment, it is becoming a part of our common life style. And tremendous information is cumulated rapidly. In these trends, it is becoming a very important technology to find out exact information in a large data to present users. Collaborative filtering is the method based on other users' preferences, can not only reflect exact attributes of user but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, we propose clustering method by user's features based on SOM for predicting purchase pattern in u-Commerce. it is necessary for us to make the cluster with similarity by user's features to be able to reflect attributes of the customer information in order to find the items with same propensity in the cluster rapidly. The proposed makes the task of clustering to apply the variable of featured vector for the user's information and RFM factors based on purchase history data. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.470-479
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• 2010

In relation to the extraction of opinion words, it may be difficult to directly apply most of the methods suggested in existing English studies to the Korean language. Additionally, the manual method suggested by studies in Korea poses a problem with the extraction of opinion words in that it takes a long time. In addition, English thesaurus-based extraction of Korean opinion words leaves a challenge to reconsider the deterioration of precision attributed to the one to one mismatching between Korean and English words. Studies based on Korean phrase analyzers may potentially fail due to the fact that they select opinion words with a low level of frequency. Therefore, this study will suggest the k-Structure (k=5 or 8) method, which may possibly improve the precision while mutually complementing existing studies in Korea, in automatically extracting opinion words from a simple sentence in a given Korean product review. A simple sentence is defined to be composed of at least 3 words, i.e., a sentence including an opinion word in ${\pm}2$ distance from the attribute name (e.g., the 'battery' of a camera) of a evaluated product (e.g., a 'camera'). In the performance experiment, the precision of those opinion words for 8 previously given attribute names were automatically extracted and estimated for 1,868 product reviews collected from major domestic shopping malls, by using k-Structure. The results showed that k=5 led to a recall of 79.0% and a precision of 87.0%; while k=8 led to a recall of 92.35% and a precision of 89.3%. Also, a test was conducted using PMI-IR (Pointwise Mutual Information - Information Retrieval) out of those methods suggested in English studies, which resulted in a recall of 55% and a precision of 57%.