• Korean Journal of Environmental Agriculture
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• v.32 no.3
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• pp.214-223
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• 2013

BACKGROUND: This study focused on the development of an analytical method about dichlorprop (DCPP; 2-(2,4-dichlorophenoxy)propionic acid) which is a plant growth regulator, a synthetic auxin for agricultural commodities. DCPP prevents falling of fruits during their growth periods. However, the overdose of DCPP caused the unwanted maturing time and reduce the safe storage period. If we take fruits with exceeding maximum residue limits, it could be harmful. Therefore, this study presented the analytical method of DCPP in agricultural commodities for the nation-wide pesticide residues monitoring program of the Ministry of Food and Drug Safety. METHODS AND RESULTS: We adopted the analytical method for DCPP in agricultural commodities by gas chromatograph in cooperated with Electron Capture Detector(ECD). Sample extraction and purification by ion-associated partition method were applied, then quantitation was done by GC/ECD with DB-17, a moderate polarity column under the temperature-rising condition with nitrogen as a carrier gas and split-less mode. Standard calibration curve presented linearity with the correlation coefficient ($r^2$) > 0.9998, analysed from 0.1 to 2.0 mg/L concentration. Limit of quantitation in agricultural commodities represents 0.05 mg/kg, and average recoveries ranged from 78.8 to 102.2%. The repeatability of measurements expressed as coefficient of variation (CV %) was less than 9.5% in 0.05, 0.10, and 0.50 mg/kg. CONCLUSION(S): Our newly improved analytical method for DCPP residues in agricultural commodities was applicable to the nation-wide pesticide residues monitoring program with the acceptable level of sensitivity, repeatability and reproducibility.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.1
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• pp.40-46
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• 2013

The isotope ratios of $^{13}C/^{12}C$ and $^{18}O/^{16}O$ for a sample in a mass spectrometer are measured relative to those of a pure $CO_2$ reference gas (i.e., laboratory working standard). Thus, the calibration of a laboratory working standard gas to the international isotope scales (Pee Dee Belemnite (PDB) for ${\delta}^{13}C$ and Vienna Standard Mean Ocean Water (V-SMOW) for ${\delta}^{18}O$) is essential for comparisons between data sets obtained by other groups on other mass spectrometers. However, one often finds difficulties in getting well-calibrated standard gases, because of their production time and high price. Additional difficulty is that fractionation processes can occur inside the gas cylinder most likely due to pressure drop in long-term use. Therefore, studies on laboratory production of pure $CO_2$ isotope standard gas from stable solid calcium carbonate standard materials, have been performed. For this study, we propose a method to extract pure $CO_2$ gas without isotope fractionation from a solid calcium carbonate material. The method is similar to that suggested by Coplen et al., (1983), but is better optimized particularly to make a large amount of pure $CO_2$ gas from calcium carbonate material. The $CaCO_3$ releases $CO_2$ in reaction with 100% pure phosphoric acid at $25^{\circ}C$ in a custom designed, evacuated reaction vessel. Here we introduce optimal procedure, reaction conditions, and samples/reactants size for calcium carbonate-phosphoric acid reaction and also provide the details for extracting, purifying and collecting $CO_2$ gas out of the reaction vessel. The measurements for ${\delta}^{18}O$ and ${\delta}^{13}C$ of $CO_2$ were performed at Seoul National University using a stable isotope ratio mass spectrometer (VG Isotech, SIRA Series II) operated in dual-inlet mode. The entire analysis precisions for ${\delta}^{18}O$ and ${\delta}^{13}C$ were evaluated based on the standard deviations of multiple measurements on 15 separate samples of purified $CO_2$. The pure $CO_2$ samples were taken from 100-mg aliquots of a solid calcium carbonate (Solenhofen-ori $CaCO_3$) during 8-day experimental period. The multiple measurements yielded the $1{\sigma}$ precisions of ${\pm}0.01$‰ for ${\delta}^{13}C$ and ${\pm}0.05$‰ for ${\delta}^{18}O$, comparable to the internal instrumental precisions of SIRA. Therefore, we conclude the method proposed in this study can serve as a way to produce an accurate secondary and/or laboratory $CO_2$ standard gas. We hope this study helps resolve difficulties in placing a laboratory working standard onto the international isotope scales and does make accurate comparisons with other data sets from other groups.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Chest Surgery
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• v.35 no.10
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• pp.712-723
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• 2002

Substantial alterations in cerebral blood flow(CBF) are known to occur during cardiopulmonary bypass(CPB). Many investigators have speculated that these changes may be responsible for both minor and major cerebral damages after CPB. More recently, these changes in CBF have been observed to be intimately related to the arterial carbon dioxide tension(Pa$CO_2$) maintained during CPB. The present study was prospectively designed to investigate the clinical effects of normocapnic and hypercapnic CPB on the cerebral oxygen metabolism in cardiac surgery Material and Method: Thirty-six adult patients scheduled for elective cardiac surgery were randomized to either normocapnic group (Pa$CO_2$35~40 mmHg, n＝18) or hypercapnic group(Pa$CO_2$, 45~55 mmHg, n＝18) with moderately hypothermic nonpulsatile CPB(nasopharyngeal temperature of 29~3$0^{\circ}C$). In each patient, middle cerebral artery blood flow velocity( $V_{MCA}$), cerebral arteriovenous oxygen content difference (C(a-v) $O_2$), cerebral oxygen extraction(COE), cerebral metabolic rate for oxygen(CMR $O_2$), cerebral oxygen transport( $T_{E}$ $O_2$), $T_{E}$ $O_2$/CMR $O_2$ ratio, cerebral desaturation(internal jugular bulb blood oxygen saturation $\leq$ 50%), and arterial and jugular bulb blood gas were evaluated throughout the operation. Postoperative neuropsychologic complications were assessed in all patients. All variables were compared between the two groups. Result: VMCA(169.13 $\pm$ 8.32 vs 153.11 $\pm$8.98%), TE $O_2$(1,911.17$\pm$250.14 vs 1,757.40$\pm$249.56), $T_{E}$ $O_2$,/CMR $O_2$ ratio(287.38$\pm$28.051 vs 246.77$\pm$25.84), $O_2$ tension in internal jugular bulb (41.66$\pm$9.19 vs 31.50$\pm$6.09 mmHg), and $O_2$saturation in internal jugular bulb(68.97$\pm$10.96 vs 58.12$\pm$12.11%) during CPB were significantly lower in normocapnic group(p＝0.03), whereas hypercapnic group had lower C(a-v) $O_2$(3.9$\pm$0.3 vs 4.9$\pm$0.3 mL/dL), COE(0.3$\pm$0.03 vs 0.4$\pm$0.03), CMR $O_2$(5.8 $\pm$0.5 vs 6.8$\pm$0.6), and arterial blood pH(7.36$\pm$0.09 vs 7.46$\pm$0.07, p＝0.04) during CPB. Hypercapnic group had lower incidence of cerebral desaturation than normocapnic group(3 vs 9 patients, p=0.03). Duration of the neuropsychologic complication(delirium) were shorter in hypercapnic group than in normocapnic group(36 vs 60 hrs, p＝0.009). Conclusion: These findings suggest that hypercapnic CPB may have salutary effects on the cerebral oxygen metabolism and postoperative neurologic outcomes in cardiac surgery.surgery.

• Applied Biological Chemistry
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• v.17 no.2
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• pp.125-137
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• 1974

The chemical structure of glycolipid of Selenomonas ruminantium cell wall was to be elucidated. The bacterial cells were treated in hot TCA and the glycolipid fractions were extracted by the solvent $CHCl_3\;:\;CH_3OH$ (1 : 3). The extracted glycolipids fraction was further separated by acetone extraction. The acetone soluble fraction was named as the spot A-compound. The acetone insoluble but ether soluble fraction was named as the spot B-compound. These two compounds were examined for elucidation of their chemical structure. The results were as follows: 1. The IR spectral analysis showed that O-acyl and N-acyl fatty acids were linked to glucosamine moiety in the spot A-compound. However in the spot B-compound in addition to O and N-acyl acids phosphorus was shown to be attached to glucosamine. 2. It was recognized by gas liquid chromatography that spot A compound contained beta-OH $C_{13:0}$ fatty acid in predominance in addition to the fatty acid with beta-OH $C_{9:0}$, whereas the spot B compound was composed of the predominant fatty acid of beta-OH $C_{13:0}$ with small amount of beta-OH $C_{9:0}$. 3. According to the paper chromatographic analysis of hydrazinolysis products of the spot A compound, a compound of a similar Rf value as the chitobiose was recognized, which indicated a structure of two molecules glucosamine condensed. The low Rf value of the hydrazinolysis product of the spot B-compound confirmed the presence of phosphorus attached to glucosamine. 4. The appearance of arabinose resulting from. ninhydrin decomposition of the acid hydrolyzate of the spot A compound indicated that the amino group is attached to $C_2$ of glucosamine. 5. The amount of glucosamine in the N-acetylated spot A compound decreased in half of the original content by the treatment. with $NaBH_4$, indicating that there are two molecules of glucosamines in the spot A compound. The presence of 1, 6-linkage between two molecules of glucosamine was suggested by the Morgan-Elson reaction and confirmed by the periodate decomposition test. 6. By the action of ${\beta}-N-acetyl$ glucosaminidase the N-acetylated spot A compound was completely decomposed into N-acetyl glucosamine, whereas the spot B compound was not. This indicated the spot A compound has a beta-linkage. 7. When phosphodiesterase or phosphomonoesterase acted on $^{32}P-labeled$ spot B compound, $^{32}P$ was not released by phosphodiesterase, but completely released by phosphomonoesterase. This indicated that one phosphorus is linked to glucosamine moiety. 8. The spot A compound is assumed to have the following chemical structure: That is glucosaminyl, ${\beta}-1$, 6-glucosamine to which O-acyl and N-acyl fatty acids are linked, of which the predominant fatty acid is beta-OH $C_{13:0}$ fatty acid in addition to beta-OH $C_{9:0}$ fatty acid 9. The spot B compound is likely to have the linkage of $glucosaminyl-{\beta}-1$, 6-glucosamine to which phosphorus is linked in monoester linkage. Furthermore both O-acyl and N-acyl fatty acids contained beta-OH $C_{13:0}$ fatty acid predominantly in addition to beta-OH $C_{9:0}$ fatty acid.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.20 no.1
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• pp.41-51
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• 1987

This study was carried out to prepare powdered smoked-dried mackerel which can be used as a soup base, and to examine storage stability and the taste compounds of Products. Raw mackerel are filleted, toiled for 10 minutes and pressed to remove lipids, and then soaked in extract solution of skipjack meat. This soaked mackerel are smoked 3 times to $10-12\%$ moisture content at $80^{\circ}C$ for 8 hours. And the smoked-dried mackerel were pulverized to 50 mesh. Finally, the powdered smoked-dried mackerel were packed in a laminated film $bag(PET/Al\;foil/CPP:\;5{\mu}m/15{\mu}m/70{\mu}m,\;15\times17cm)$ with air(product C), nitrogen(product N) and oxygen absorber(product O), and then stored at room temperature for 100 days. The moisture and crude lipid content of powdered smoked-dried mackerel was $11.3-12.3\%,\;12\%$, respectively, and water activity is 0.52-0.56. And these values showed little changes during storage. The pH, VBN and amino nitrogen content increased slowly during storage. Hydrophilic and lipophilic brown pigment formation showed a tendency of increase in product(C) and showed little change in product(N) and (O). The TBA value, peroxide value and carbonyl value of product(N) and (O) were lower than those of product (C). The major fatty acids of products were 16:0, 18:1, 22:6, 18:0 and 20:5, and polyenoic acids decreased, while saturated and monoenoic acids increased during processing and storage of products. The IMP content in products were 420.2-454.2 mg/100 g and decreased slightly with storage period. And major non-volatile organic acids in products were lactic acid, succinic acid and $\alpha-ketoglutaric$ acid. In free amino acids and related compounds, major ones are histidine, alanine, hydroxyproline, lysine, glutamic acid and anserine, which occupied $80.8\%$ of total free amino acids. The taste compounds of powdered smoked-dried mackerel were free amino acids and related compounds (1,279.4 mg/100 g), non-volatile organic acids(948.1 mg/100 g), nucleotides and their related compounds (672.8 mg/100 g), total creatinine(430.4 ntg/100 g), tetaine(86.6 mg/100 g) and small amount of TMAO. The extraction condition of powdered smoked-dried mackerel in preparing soup stock is appropriate at $100^{\circ}C$ for 1 minute. Judging from the results of taste and sensory evaluation, it is concluded that the powdered smoked-dried mackerel can be used as natural flavoring substance in preparing soups and broth.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.29 no.5
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• pp.301-314
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• 2003

The purposes of this retrospective study were to assess the change of serum parameters in oral and maxillofacial surgery patients after operation and to determine what laboratory parameters on treatment periods were associated with the recovery of systemic condition. For purposes of assessing systemic nutritional status, several serum parameters were chosen. The sample patients were randomsubjects extracted from three category patient groups- oral cancer, odontogenic abscess, facial bone fracture based on treated patients at department of oral and maxillofacial surgery in Pusan National University Hospital from September 1, 1998, to September 1, 2002. Each groups were consisted with 10 patients. Each patient chart was examined and blood sample parameters were reviewed with clinical signs, symptoms and vital sign at preoperative day, postoperative 1 day, postoperative 1 week. Several parameters were analyzed statistically for extraction of mean values and differences between the periods groups. The findings of serum parameters of cancer, abscess and fracture groups were as follows: 1. In cancer patients, Hb, MCV, albumin, cholesterol, LDH, AST, ALT, neutrophil, platelet, leukocyte, Na, K, Cl, BUN, creatinine were analyzed. Values of Hb, albumin, AST, neutrophil, leukocyte, Cl showed significantly differences according to periods. 2. In abscess patients, CRP, ESR, leukocyte, body temperature, neutrophil were analyzed. Values of CRP, leukocyte, body temperature, neutrophil showed significanlty differences according to periods. 3. In fracture patients, same parameters with cancer patient's were chosen. Values of platelet, Cl only showed significantly differences according to periods. 4. In cancer patients, data regarding correlation was analyzed statistically as Pearson's value. A positive correlation was found between Hb and albumin, K, Na(P<0.05). A positive correlation was also found between neutrophil and leukocyte(P<0.05). Positive correlations were found between cholesterol and ALT, LDH and platelet, creatinine both, Platelet and BUN, Na and K(P<0.01). 5. In abscess patients, Peason's correlation values were analyzed on parameters. A positive correlation was found only between CRP and neutrophil(P<0.05). 6. In fracture patients, The correlations of parameters also were statistically analyzed. Positive correlations were found between MCV and K, albumin and LDH, AST and three parameters of creatinine, Na, Cl, K and neutrophil, neutrophil and three parameters of leukocyte, BUN, K(P<0.05). Positive correlations were found between LDH and AST, ALT and AST, creatinine both(P<0.01). This retrospective clinical study showed the CRP levels only on abscess patients may be useful in determination of clinical infected status, but the levels of other parameters on cancer, fracture patients did not showed significant values as diagnostic aids for clinical status.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.