• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.15-37
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• 2004

With the growth in the size of datasets, data mining has recently become an important research topic. Especially, interests about spatio-temporal data mining has been increased which is a method for analyzing massive spatio-temporal data collected from a wide variety of applications like GPS data, trajectory data of surveillance system and earth geographic data. In the former approaches, conventional clustering algorithms are applied as spatio-temporal data mining techniques without any modification. In this paper, we focused to SOM that is the most common clustering algorithm applied to clustering analysis in data mining wet and develop the spatio-temporal data mining module based on it. In addition, we analyzed the clustering results of developed SOM module and compare them with those of K-means and Agglomerative Hierarchical algorithm in the aspects of homogeneity, separation, separation, silhouette width and accuracy. We also developed specialized visualization module fur more accurate interpretation of mining result.

• Journal of Environmental Health Sciences
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• v.47 no.2
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• pp.123-130
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• 2021

Objectives: Control methods against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) aerosols have been introduced. Airborne spreading theories for SARS-CoV-2 were analyzed in this study. Methods: Control methods for airborne microorganisms were discussed. Studies on theoretical estimations for airborne spreading of SARS-CoV-2 were presented and analyzed. Analytic calculations were conducted for explaining control techniques for airborne microorganisms. Results: Control methods for SARS-CoV-2 aerosols can include physical or biological procedures. Characterization of SARS-CoV-2 aerosols and massive clustering infection cases of COVID-19 support the airborne spreading theories of SARS-CoV-2. It is necessary to consider the disadvantages of control methods for airborne microorganisms. Conclusions: A study on control methods against bioaerosols is necessary to prevent the spreading of viruses. Airborne spreading theories of SARS-CoV-2 were supported by the current evidence, but further studies are needed to confirm these theories.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.1
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• pp.45-58
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• 2005

This Paper is a comparative study of feature selection methods for Korean web documents clustering. First, we focused on how the term feature and the co-link of web documents affect clustering performance. We clustered web documents by native term feature, co-link and both, and compared the output results with the originally allocated category. And we selected term features for each category using $X^2$, Information Gain (IG), and Mutual Information (MI) from training documents, and applied these features to other experimental documents. In addition we suggested a new method named Max Feature Selection, which selects terms that have the maximum count for a category in each experimental document, and applied $X^2$ (or MI or IG) values to each term instead of term frequency of documents, and clustered them. In the results, $X^2$ shows a better performance than IG or MI, but the difference appears to be slight. But when we applied the Max Feature Selection Method, the clustering Performance improved notably. Max Feature Selection is a simple but effective means of feature space reduction and shows powerful performance for Korean web document clustering.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.148-150
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• 2017

In recent years, "altmetrics", given birth by social media and the academic community, have become a metric source for measuring the academic impact of scientific literature. This study has undertaken a co-word analysis of author keywords in "Altmetrics" articles from the Web of Science database from 2012 to 2017 and used a co-occurrence matrix to create a clustering of the words. "Altmetrics" co-occurrence network map was derived and the research hotspots was analyzed.

• Journal of KIISE:Software and Applications
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• v.33 no.2
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• pp.243-251
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• 2006

In multi-document summarization by means of salient sentence extraction, it is important to remove redundant information. In the removal process, the similarities and differences of sentences are considered. In this paper, we propose a method for multi-document summarization which extracts salient sentences without having redundant sentences by way of cohesive term clustering method that utilizes co-occurrence Information. In the cohesive term clustering method, we assume that each term does not exist independently, but rather it is related to each other in meanings. To find the relations between terms, we cluster sentences according to topics and use the co-occurrence information oi terms in the same topic. We conduct experimental tests with the DUC(Document Understanding Conferences) data. In the tests, our method shows better performance of summarization than other summarization methods which use term co-occurrence information based on term cohesion of document or sentence unit, and simple statistical information.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.12
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• pp.1493-1503
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• 1999

ATM Clustering System과 같이 SAN(System Area Network) 환경에서 동작하는 시스템은 낮은 지연시간과 넓은 대역폭의 네트워크가 필수적이나 기존의 에러 복구 프로토콜들은 이러한 요구를 충족시키기에는 큰 오버헤드를 가지고 있다. 제안된 새로운 에러 복구 프로토콜은 ATM Clustering System 환경에서 최적의 성능을 나타내는 light-weight 프로토콜로 에러가 없는 상황과 에러 복구가 진행중인 상황에 따라 acknowledgement 주기를 적응적으로 변화시키는 adaptive acknowledgement scheme를 제안하여 적용하였다. 제안된 프로토콜은 상용 툴인 SDT를 이용한 논리 검증 받았고, DEVSim++ 환경에서의 성능 분석을 통해 프로토콜이 최상의 성능을 보이기 위한 파라메터 값을 찾았고, 이 값을 적용하였을 때의 성능을 기존의 프로토콜과 비교하여 제안된 프로토콜이 더 우수함을 확인하였다.Abstract While a system working with SAN, such as ATM Clustering System, requires a network with low latency and wide bandwidth, the previous error recovery protocols have a serious network overhead to satisfy this requirement. The suggested error recovery protocol is a light-weight protocol which can shows its best performance at ATM Clustering System and uses a newly suggested adaptive acknowledgement scheme. In the adaptive acknowledgement scheme, the period of acknowledgement is dynamically changed depending on the state of the network. We proved the logical correctness of our protocol with SDT and did performance analysis with DEVSim++. From the analysis, we found the optimal parameter values for best performance and showed that our protocol works better than the previous error recovery protocols.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.489-498
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• 2006

Clustering results differ according to the datasets and the performance worsens even while using web documents which are manually processed by an indexer, because although representative clusters for a feature can be obtained by statistical feature selection methods, irrelevant features(i.e., non-obvious features and those appearing in general documents) are not eliminated. Those irrelevant features should be eliminated for improving clustering performance. Therefore, this paper proposes three feature-filtering algorithms which consider feature values per document set, together with distribution, frequency, and weights of features per document set: (l) features filtering algorithm in a document (FFID), (2) features filtering algorithm in a document matrix (FFIM), and (3) a hybrid method combining both FFID and FFIM (HFF). We have tested the clustering performance by feature selection using term frequency and expand co link information, and by feature filtering using the above methods FFID, FFIM, HFF methods. According to the results of our experiments, HFF had the best performance, whereas FFIM performed better than FFID.

• Korean Journal of Ecology and Environment
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• v.57 no.2
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• pp.83-91
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• 2024

This study utilizes association rule learning and clustering analysis to explore the co-occurrence and relationships within ecosystems, focusing on the endangered brackish-water snail Clithon retropictum, classified as Class II endangered wildlife in Korea. The goal is to analyze co-occurrence patterns between brackish-water snails and other species to better understand their roles within the ecosystem. By examining co-occurrence patterns and relationships among species in large datasets, association rule learning aids in identifying significant relationships. Meanwhile, K-means and hierarchical clustering analyses are employed to assess ecological similarities and differences among species, facilitating their classification based on ecological characteristics. The findings reveal a significant level of relationship and co-occurrence between brackish-water snails and other species. This research underscores the importance of understanding these relationships for the conservation of endangered species like C. retropictum and for developing effective ecosystem management strategies. By emphasizing the role of a data-driven approach, this study contributes to advancing our knowledge on biodiversity conservation and ecosystem health, proposing new directions for future research in ecosystem management and conservation strategies.

• Journal of The Korean Astronomical Society
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• v.29 no.1
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• pp.75-81
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• 1996

We mapped the $^{13}CO$ line in the dark nebula L134 using the 14-m Taeduck radio telescope with a 57 arcsec beam and one beam spacing. The cloud has a spherical shape with an intensity peak ridge extended from the northwest to the southeast directions. The halfwidth and the radial velocity of the lines peak at the region of the cloud center. The radial velocity decreases from the cloud center towards the north and south directions. The integrated line intensity distributions in the space-velocity plane show some structure and a velocity gradient. The $^{13}CO$ and $H_2CO$ clouds and dark clouds are closely related in space in shape, outer boundary, and intensity peak positions. The $^{13}CO$ integrated line intensity is linearly proportional to the visual extinction.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.5
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• pp.104-113
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• 2006

The main objective of this study is to investigate the effect of compounded welding by using acoustic emission (AE) signals and doing a source location for weld heat affected zone (HAZ) through tensile testing. This study was carried out an SWS 490A high strength steel for electric shield metal arc welding, SMAW; $CO_2$ gas metal arc welding, GMAW($CO_2$); and gas tungsten arc welding, GTAW/TIG. Data displays are based on the measured parameters of the AE signals, along with environmental variables such as time and load. For instance, Gutenberg-Richter magnitude-frequency relationship (G-R MFR) offers useful b-value in data analysis. Namely event identification, source location gives the X- and Y-coordinates of the AE source. And K-means clustering analysis by Euclidean distance confirmed that was powerful to source location. Generally, strength of welded metal zone was stronger than strength of base metal. As the result, confirmed certainly that fracture is produced in HAZ instead of welded metal zone from source location.