• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1151-1160
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• 2006

The similarity weight, the pearson's correlation coefficient, which is used in the recommender system has a weak point that it cannot predict all of the prediction value. The similarity weight, the vector similarity, has a weak point of the high MAE although the prediction coverage using the vector similarity is higher than that using the pearson's correlation coefficient. The purpose of this study is to suggest how to raise the prediction coverage. Also, the MAE using the suggested method in this study was compared both with the MAE using the pearson's correlation coefficient and with the MAE using the vector similarity, so was the prediction coverage. As a result, it was found that the low of the MAE in the case of using the suggested method was higher than that using the pearson's correlation coefficient. However, it was also shown that it was lower than that using the vector similarity. In terms of the prediction coverage, when the suggested method was compared with two similarity weights as I mentioned above, it was found that its prediction coverage was higher than that pearson's correlation coefficient as well as vector similarity.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1129-1139
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• 2006

Pearson's correlation coefficient and vector similarity are generally applied to The users' similarity weight of user based recommender system. This study is needed to find that the correlation coefficient of similarity weight is effected by the number of pair response and significance probability. From the classified correlation coefficient by the significance probability test on the correlation coefficient and pair of response, the change of MAE is studied by comparing the predicted precision of the two. The results are experimentally related with the change of MAE from the significant correlation coefficient and the number of pair response.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.119-128
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• 2006

The similarity weight, the pearson's correlation coefficient, which is used in the recommender system has a weak point that it cannot predict all of the prediction value. The similarity weight, the vector similarity, has a weak point of the high MAE although the prediction coverage using the vector similarity is higher than that using the pearson's correlation coefficient. The purpose of this study is to suggest how to raise the prediction coverage. Also, the MAE using the suggested method in this study was compared both with the MAE using the pearson's correlation coefficient and with the MAE using the vector similarity, so was the prediction coverage. As a result, it was found that the low of the MAE in the case of using the suggested method was higher than that using the pearson's correlation coefficient. However, it was also shown that it was lower than that using the vector similarity In terms of the prediction coverage, when the suggested method was compared with two similarity weights as I mentioned above, it was found that its prediction coverage was higher than that pearson's correlation coefficient as well as vector similarity.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.105.1-105.1
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• 2014

The study on linear correlation between auxiliary channels in LIGO data has been performed by using Pearson's correlation coefficient, generating a correlation map.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1649-1667
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• 2012

In order to improve the accuracy and universality of the flow metric correlation analysis, this paper firstly analyzes the characteristics of Internet flow metrics as random variables, points out the disadvantages of Pearson Correlation Coefficient which is used to measure the correlation between two flow metrics by current researches. Then a method based on Symmetrical Uncertainty is proposed to measure the correlation between two flow metrics, and is extended to measure the correlation among multi-variables. Meanwhile, the simulation and polynomial fitting method are used to reveal the threshold value between different correlation degrees for SU method. The statistical analysis results on the common flow metrics using several traces show that Symmetrical Uncertainty can not only represent the correct aspects of Pearson Correlation Coefficient, but also make up for its shortcomings, thus achieve the purpose of measuring flow metric correlation quantitatively and accurately. On the other hand, reveal the actual relationship among fourteen common flow metrics.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.59-69
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• 2006

Pearson's correlation coefficient and Vector similarity are generally applied to The users' similarity weight of user based recommender system. This study is needed to find that the correlation coefficient of similarity weight is effected by the number of pair response and significance probability. From the classified correlation coefficient by the significance probability test on the correlation coefficient and pair of response, the change of MAE is studied by comparing the predicted precision of the two. The results are experimentally related with the change of MAE from the significant correlation coefficient and the number of pair response.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.12
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• pp.1199-1206
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• 2015

In this study, the exhaust gas of a diesel engine operating on biodiesel(BD) fuel(a mixture of diesel and soybean oil) was investigated for different fuel mixing ratios in the range of BD3 to BD100. The experiments were conducted using injection pressures of 400, 600, 800, 1000, and 1200 bar. The Pearson correlation coefficient and Spearman rank-order correlation coefficient were used to quantify the NOx and Soot emissions based on the fuel mixing ratio and injection pressure. Consequently, the Pearson correlation coefficient obtained for NOx and Soot emissions according to the mixing ratio and injection pressure was -0.811 and the corresponding Spearman rank-order correlation coefficient was -0.884, which indicated that the correlation of the NOx and Soot emissions was linear. Thus, the NOx and Soot have a trade-off relationship. Moreover, at each injection pressure, the Pearson correlation coefficient was a negative number, which indicated an inversely proportional relationship between NOx and Soot.

• Journal of Yeungnam Medical Science
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• v.35 no.1
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• pp.63-69
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• 2018

Background: Kinovea software that tracking semi-automatically the motion in video screen has been used to study motion-related tasks in several studies. However, the validation of this software in open field test to assess locomotor activity have not been studied yet. Therefore, this study aimed to examine the reliability and validity of this software in analyzing locomotor activities. Methods: Thirty male Institute Cancer Research mice were subjected in this study. The results examined by this software and the classical method were compared. Test-retest reliability and inter-rater reliability were analyzed with Pearson's correlation coefficient and intraclass correlation coefficient (ICC). The validity of this software was analyzed with Pearson's correlation coefficient. Results: This software showed good test-retest reliability (ICC=0.997, 95% confidence interval [CI]=0.975-0.994, p<0.001). This software also showed good inter-rater reliability (ICC=0.987, 95% CI=0.973-0.994, p<0.001). Furthermore, in three analyses for the validity of this software, there were significant correlations between two methods (Pearson's correlation coefficient=0.928-0.972, p<0.001). In addition, this software showed good reliability and validity in the analysis locomotor activity according to time interval. Conclusion: This study showed that this software in analyzing drug-induced locomotor activity has good reliability and validity. This software can be effectively used in animal study using the analysis of locomotor activity.

• Journal of Periodontal and Implant Science
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• v.43 no.4
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• pp.153-159
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• 2013

Purpose: The level of the implant above the marginal bone and flap design have an effect on the bone resorption during the healing period. The aim of this study is to detect the relationship between the level of the implant at the implant placement and the bone level at the healing period in the mesial and distal side of implants placed with flapless (FL) and full-thickness flap (FT) methods. Methods: Twenty-two nonsubmerged implants were placed with the FL and FT technique. Periapical radiographs were taken of the patient at implant placement, and at 6 and 12 weeks. By using computer software, bone level measurements were taken from the shoulder of the healing cap to the first bone implant contact in the mesial and distal side of the implant surface. Results: At 6 weeks, the correlation between the crestal bone level at the implant placement and crestal bone level of the FT mesially was significant (Pearson correlation coefficient=0.675, P<0.023). At 12 weeks, in the FT mesially, the correlation was nonsignificant (Spearman correlation coefficient=0.297, P<0.346). At 6 weeks in the FT distally, the correlation was nonsignificant (Pearson correlation coefficient=0.512, P<0.107). At 12 weeks in the FT distally, the correlation was significant (Spearman correlation coefficient=0.730, P<0.011). At 6 weeks in the FL mesially, the correlation was nonsignificant (Spearman correlation coefficient=0.083, P<0.809). At 12 weeks in the FL mesially, the correlation was nonsignificant (Spearman correlation coefficient= 0.062, P<0.856). At 6 weeks in the FL distally, the correlation was nonsignificant (Spearman correlation coefficient=0.197, P<0.562). At 12 weeks in the FL distally, the correlation was significant (Pearson correlation coefficient=0.692, P<0.018). Conclusions: A larger sample size is recommended to verify the conclusions in this preliminary study. The bone level during the healing period in the FT was more positively correlated with the implant level at implant placement than in the FL.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.143-149
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• 2022

This excerpt delineates a quantitative measure of relationship between a research title and its respective abstract extracted from different journal articles documented through a Korean Citation Index (KCI) database published through various journals. In this paper, we propose a machine learning-based similarity metric that does not assume normality on dataset, realizes the imbalanced dataset problem, and zero-variance problem that affects most of the rule-based algorithms. The advantage of using this algorithm is that, it eliminates the limitations experienced by Pearson correlation coefficient (r) and additionally, it solves imbalanced dataset problem. A total of 107 journal articles collected from the database were used to develop a corpus with authors, year of publication, title, and an abstract per each. Based on the experimental results, the proposed algorithm achieved high correlation coefficient values compared to others which are cosine similarity, euclidean, and pearson correlation coefficients by scoring a maximum correlation of 1, whereas others had obtained non-a-number value to some experiments. With these results, we found that an effective title must have high correlation coefficient with the respective abstract.