• Journal of Advanced Navigation Technology
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• v.18 no.5
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• pp.468-475
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• 2014

The recommend system is getting more difficult real time recommend by lager preference data set, computing power and recommend algorithm. For this reason, recommend system is proceeding actively one's studies toward distribute processing method of large preference data set. This paper studied distribute processing method of large preference data set using hadoop distribute processing platform and mahout machine learning library. The recommend algorithm is used Co-occurrence Matrix similar to item Collaborative Filtering. The Co-occurrence Matrix can do distribute processing by many node of hadoop cluster, and it needs many computation scale but can reduce computation scale by distribute processing. This paper has simplified distribute processing of co-occurrence matrix by changes over from four stage to three stage. As a result, this paper can reduce mapreduce job and can generate recommend file. And it has a fast processing speed, and reduce map output data.

• 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 Ecology and Environment
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• v.47 no.1
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• pp.1-13
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• 2023

Background: The positive effects of Arctic plants on the soil environment and plant-species co-occurrence patterns are known to be particularly important in physically harsh environments. Although three dominant plants (Cassiope tetragona, Dryas octopetala, and Silene acaulis) are abundant in the Arctic ecosystem at Ny-Ålesund, Svalbard, few studies have examined their occurrence patterns with other species and their buffering effect on soil-temperature and soil-moisture fluctuation. To quantify the plant-species co-occurrence patterns and their positive effects on soil environments, I surveyed the vegetation cover, analyzed the soil-chemical properties (total carbon, total nitrogen, pH, and soil organic matter) from 101 open plots, and measured the daily soil-temperature and soil-moisture content under three dominant plant patches and bare soil. Results: The Cassiope tetragona and Dryas octopetala communities increased the soil-temperature stability; however, the three dominant plant communities did not significantly affect the soil-moisture stability. Non-metric multidimensional scaling separated the sampling sites into three groups based on the different vegetation compositions. The three dominant plants occurred randomly with other species; however, the vegetation composition of two positive co-occurring species pairs (Oxyria digyna-Cerastium acrticum and Luzula confusa-Salix polaris) was examined. The plant species richness did not significantly differ in the three plant communities. Conclusions: The three plant communities showed distinctive vegetation compositions; however, the three dominant plants were randomly and widely distributed throughout the study sites. Although the facilitative effects of the three Arctic plants on increases in the soil-moisture fluctuation and richness were not quantified, this research enables a deeper understanding of plant co-occurrence patterns in Arctic ecosystems and thereby contributes to predicting the shift in vegetation composition and coexistence in response to climate warming. This research highlights the need to better understand plant-plant interactions within tundra communities.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.5 no.3
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• pp.76-85
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• 2024

The purpose of this study is to propose a new method of analysis focusing on interconnections between species rather than traditional biodiversity analysis, which represents ecosystems in terms of species and individual counts such as species diversity and species richness. This new approach aims to enhance our understanding of ecosystem networks. Utilizing data from the 4th National Natural Environment Survey (2014-2018), the following eight taxonomic groups were targeted for our study: herbaceous plants, woody plants, butterflies, Passeriformes birds, mammals, reptiles & amphibians, freshwater fishes, and benthonic macroinvertebrates. A co-occurrence frequency analysis was conducted using nationwide data collected over five years. As a result, in all eight taxonomic groups, the degree value represented by a linear regression trend line showed a slope of 0.8 and the weighted degree value showed an exponential nonlinear curve trend line with a coefficient of determination (R²) exceeding 0.95. The average value of the clustering coefficient was also around 0.8, reminiscent of well-known social phenomena. Creating a combination set from the species list grouped by temporal information such as survey date and spatial information such as coordinates or grids is an easy approach to discern species distributed regionally and locally. Particularly, grouping by species or taxonomic groups to produce data such as co-occurrence frequency between survey points could allow us to discover spatial similarities based on species present. This analysis could overcome limitations of species data. Since there are no restrictions on time or space, data collected over a short period in a small area and long-term national-scale data can be analyzed through appropriate grouping. The co-occurrence frequency analysis enables us to measure how many species are associated with a single species and the frequency of associations among each species, which will greatly help us understand ecosystems that seem too complex to comprehend. Such connectivity data and graphs generated by the co-occurrence frequency analysis of species are expected to provide a wealth of information and insights not only to researchers, but also to those who observe, manage, and live within ecosystems.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.9-11
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• 2005

We propose a new content-based image retrieval using a block color co-occurrence matrix (BCCM) and pattern correlogram. In the proposed method, the color feature vectors are extracted by using BCCM that represents the probability of the co-occurrence of two mean colors within blocks. Also the pattern feature vectors are extracted by using pattern correlogram which is combined with spatial correlation of pattern. In the proposed pattern correlogram method. after block-divided image is classified into 48 patterns with respect to the change of the RGB color of the image, joint probability between the same pattern from the surrounding blocks existing at the fixed distance and the center pattern is calculated. Experimental results show that the proposed method can outperform the conventional methods as regards the precision and the size of the feature vector dimension.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.601-604
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• 1999

This Paper proposes the simple and efficient image retrieval algorithm using subregional texture features. In order to retrieve images in terms of its contents, it is required to obtain a precise segmentation. However, it is very difficult and takes a long computing time. Therefore. this paper proposes a simple segmentation method, which is to divide an image into high and low entropy regions by using Picture Information Measure (PIM). Also, in order to describe texture characteristics of each region, this paper suggest six different texture features produced on the basis of co-occurrence matrix. For an image retrieval system, a normalised correlation is adopted as a similarity function, which is not dependent on the range of each texture feature values. Finally, this proposed algorithm is applied to a various images and produces competitive results.

• Journal of Korea Technology Innovation Society
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• v.8 no.2
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• pp.860-885
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• 2005

This study relates to develop a quantitative method through which promising technology-based business items can be discovered and selected. For this study, we utilized patent trend analysis, technology-industry concordance analysis, and keyword co-occurrence analysis of US patents. By analyzing patent trends and technology-industry concordance, we were able to find out the emerging industry trends : prevalence of bio industry, service industry, and B2C business. From the direct and co-occurrence analysis of newly discovered patent keywords in the year, 2000, 28 promising business item candidates were extracted. Finally, the promising item candidates were prioritized using 4 business attractiveness determinants; market size, product life cycle, degree of the technological innovation, and coincidence with the industry trends. This result implicates that reliable discovery and selection of promising technology-based business items can be performed by a quantitative, objective and low- cost process using knowledge discovery method from patent database instead of peer review.

• Korean Journal of Social Welfare
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• v.68 no.2
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• pp.5-25
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• 2016

Despite previous literatures suggesting the co-occurrence of substance abuse and behavioral addiction, their relationship has not been systematically explored. Especially, college students are a high risk group for alcohol use and gambling activities and they have various psychosocial problems due to addictive behaviors. This study aimed to empirically examine that drinking problems predict gambling problems among college students. A total of 455 college students who experienced drinking and gambling completed a survey. Logistic regression analysis were performed. After adjusting for demographics and family related variables, drinking problems predicted the occurrence of problem gambling. Implications for social work practice, policy planning and research area on addiction are discussed.

• Journal of the Korean Wood Science and Technology
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• v.51 no.6
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• pp.458-469
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• 2023

Text mining techniques provide valuable insights into research information across various fields. In this study, text mining was used to identify research trends in wood science from 2012 to 2022, with a focus on representative journals published in Korea and Japan. Abstracts from Journal of the Korean Wood Science and Technology (JKWST, 785 articles) and Journal of Wood Science (JWS, 812 articles) obtained from the SCOPUS database were analyzed in terms of the word frequency (specifically, term frequency-inverse document frequency) and co-occurrence network analysis. Both journals showed a significant occurrence of words related to the physical and mechanical properties of wood. Furthermore, words related to wood species native to each country and their respective timber industries frequently appeared in both journals. CLT was a common keyword in engineering wood materials in Korea and Japan. In addition, the keywords "MDF," "MUF," and "GFRP" were ranked in the top 50 in Korea. Research on wood anatomy was inferred to be more active in Japan than in Korea. Co-occurrence network analysis showed that words related to the physical and structural characteristics of wood were organically related to wood materials.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.50 no.3
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• pp.134-139
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• 2024

We systematically reviewed the literature on the co-occurrence of squamous cell carcinoma (SCC) and Warthin's tumor (WT), thought to be quite rare, to help reduce misdiagnosis and improve treatment planning. For this systematic review, we searched for articles in the Web of Science and PubMed databases, analyzed relevant studies for forward and backward citations, and identified only articles reporting on the "co-occurrence" of WT and SCC. Of the 237 studies identified, 12 comprising 18 patients met the inclusion criteria, to which we added one study from our institution. Most WTs were associated with SCC in the parotid gland or cervical lymph nodes. Most patients (89.5%) underwent selective or radical neck dissection due to identification of lesions separate from the primary SCC. Despite its frequent co-occurrence with other neoplasms, WT in the parotid or cervical lymph nodes tends to be misdiagnosed as a metastatic node when SCC is observed as the primary tumor. Factors to consider in diagnosis and neck management include identification of an association other than growth or development by lymphangiogenesis and whether the patient is a smoker, a strong risk factor.