• Journal of the Korean Data and Information Science Society
• /
• v.17 no.4
• /
• pp.1129-1139
• /
• 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
• /
• 2006.11a
• /
• pp.59-69
• /
• 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.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.28 no.6
• /
• pp.396-399
• /
• 2015

We analyzed the correlation between breakdown voltage(BDV) of liquid nitrogen(LN2) and factors. The chosen factors affecting the breakdown are the diameter of electrode, gap length, temperature of LN2, and pressure of LN2. The BDV of LN2 was increased with increasing the diameter, the gap length and the pressure. And The BDV of LN2 was increased with decreasing the temperature. However, correlation coefficient was different from each other depending on the situation. The BDV exhibited a very high correlation coefficient of 0.92227 to dependence on the diameter. And a very high correlation coefficient of 0.94980 to dependence on the pressure under sphere(D 7.5 mm)-plane electrode. When the pressure is applied, sphere-plane electrode is the correlation coefficient was higher than that of the needle-plane electrode. It shows the dependence of a temperature coefficient of -0.758290 ~ -0.39946 under needle-plane electrode.

• Communications for Statistical Applications and Methods
• /
• v.14 no.2
• /
• pp.301-307
• /
• 2007

A effective teaching method on correlation coefficient for elementary level statistics course is discussed in this article. The well known inequalities, such as Theorem 368 of Hardy et al. (1952), are used for the interpretation of concept of covariance. An Excel example is provided for the illustration of concept of correlation coefficient.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.5
• /
• pp.1951-1966
• /
• 2020

Recent advancements in mobile device technology have enabled real-time positioning so that mobile patterns of people and favorable locations can be identified and related researches have become plentiful. One of the fields of research is the relationship between the object properties and the favored location to visit. The object properties of a person include personality, which is a major property jobs, income, gender, and age. In this study, we analyzed the relationship between the human personality and the preference of the location to visit. We used Spearman's Rank correlation coefficient, one of the many methods that can be used to determine the correlation between two variables. Instead of using actual data values, Spearman's Rank correlation coefficient deals with the ranks of the two data sets. In our research, the personality and the location data sets are used. Our personality data is ranked in five ranks and the location data is ranked in 8 ranks. Spearman's Rank correlation coefficient showed better results compared to Pearson linear correlation coefficient and Kendall rank correlation coefficient. Using Spearman's correlation coefficient, the degree of the relationship between the personality and the location preference is found to be 43%.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.4
• /
• pp.803-811
• /
• 2010

This study analyzes the characteristics of preference ratings by dividing estimated values into four groups according to rank correlation coefficient after obtaining preference estimated value to user's ratings by using collaborative filtering algorithm. It is known that the value of standard error of skewness and standard error of kurtosis lower in the group of higher rank correlation coefficient This explains that the preference of higher rank correlation coefficient has lower extreme values and the differences of preference rating values. In addition, top n recommendation lists are made after obtaining rank fitting by using the result ranks of prediction value and the ranks of real rated values, and this top n is applied to the four groups. The value of top n recommendation is calculated higher in the group of higher rank correlation coefficient, and the recommendation accuracy in the group of higher rank correlation coefficient is higher than that in the group of lower rank correlation coefficient Thus, when using standard error of skewness and standard error of kurtosis in recommender system, rank correlation coefficient can be higher, and so the accuracy of recommendation prediction can be increased.

• Journal of the Korean Home Economics Association
• /
• v.11 no.1
• /
• pp.14-25
• /
• 1973

The measurements includings 22 items such as height, weight, body width were carried out for Korean male and female students, one hundred each, from 18 to 24 years old. The correlation coefficient was calculated for every two items. The values are basic for the Clothing construction and the Pattern grading. The results are as follows : 1) The measuring values are as shown in Table 1 and the index are as shown in Table 2. 2) The correlation coefficient of length to length is larger than that of length to girth and that of length to width. The correlation coefficient of girth to girth is larger than girth to length and that of girth to width. The correlation coefficient of width to width does not show remarkable difference from those of others. 3) The correlation coefficient values of weight to lengths, weight to lengths, weight to girths and weight to width are larger. Among these, the correlation coefficient of weight to girths is the largest. 4) The correlation coefficient in general shows almost positive values except a few exception showing negative values. 5) No meaning differences are found between males and females.

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

• 한국방재학회:학술대회논문집
• /
• 2008.02a
• /
• pp.101-104
• /
• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.4
• /
• pp.228-239
• /
• 2022

Many countries around the world using ICT technologies have various technologies to keep pace with the 4th industrial revolution, and various algorithms and systems have been developed accordingly. Among them, many industries and researchers are investing in unmanned automation systems based on AI. At the time when new technology development and algorithms are developed, decision-making by big data analysis applied to AI systems must be equipped with more sophistication. We apply, Pearson's correlation analysis is applied to six independent variables to find out the job satisfaction that office workers feel according to their job characteristics. First, a correlation coefficient is obtained to find out the degree of correlation for each variable. Second, the presence or absence of correlation for each data is verified through hypothesis testing. Third, after visualization processing using the size of the correlation coefficient, the degree of correlation between data is investigated. Fourth, the degree of correlation between variables will be verified based on the correlation coefficient obtained through the experiment and the results of the hypothesis test