• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.857-868
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• 2014

Data mining is the representative analysis methodology in the era of big data, and is the process to analyze a massive volume database and summarize it into meaningful information. Association rule technique finds the relationship among several items in huge database using the interestingness measures such as support, confidence, lift, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. Moreover, we can not know association direction by them. This paper propose causally confirmed association thresholds to compensate for these problems, and then check the three conditions of interestingness measures. The comparative studies with basic association thresholds, causal association thresholds, and causally confirmed association thresholds are shown by simulation studies. The results show that causally confirmed association thresholds are better than basic and causal association thresholds.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1189-1197
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• 2013

Data mining is the process of analyzing a huge database from different perspectives and summarizing it into useful information. One of the well-studied problems in data mining is association rule generation. Association rule mining finds the relationship among several items in massive volume database using the interestingness measures such as support, confidence, lift, etc. Typical applications for this technique include retail market basket analysis, item recommendation systems, cross-selling, customer relationship management, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. This paper propose causal association thresholds to compensate for this problem, and then check the three conditions of interestingness measures. The comparative studies with basic and causal association thresholds are shown by numerical example. The results show that causal association thresholds are better than basic association thresholds.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.717-725
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• 2012

Data mining is a method to find useful information for large amounts of data in database. One of the well-studied problems in data mining is exploration for association rules. Association rule mining searches for interesting relationships among items in a given database by support, confidence, and lift. If we use the existing association rules, we can commit some errors by information loss not to consider the size of occurrence frequency. In this paper, we proposed a new association rule thresholds considering the number of possible rules of interest items and compare with existing association rule thresholds by example and real data. As the results, the new association rule thresholds were more useful than existing thresholds.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.21 no.3
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• pp.168-176
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• 2011

Objectives: Hundreds of thousands of call center workers are wearing an acoustic device for their businesses, such as telemarketing and customer counseling, and the number of the workers are increasing sharply. Because call center workers always talk to dozens of customers over the headset, they would be placed under the state of a higher risk with their hearing ability. The purpose of this study is to investigate factors that affect the hearing thresholds for the call center workers. Methods: This study investigated hearing losses of 101 workers of 5 call centers in Korea by executing puretone audiometry and self-administered questionnaires. A cross table analysis was processed to compare gender differences between male and female. Male and female hearing thresholds were compared with the Students' t-test, and one-way ANOVA was conducted to observe the difference between non-occupational and occupational characteristics in 2, 3, 4, 6, and 8 kHz hearing thresholds for the female workers. Additionally, multiple regression analysis was conducted to find the factors that affect the 4 kHz hearing thresholds. Results and Conclusions: Male hearing thresholds were higher than those of female except for 8 kHz. In the group having an ear related disease, hearing threshold of male left ear was highly affected rather than that of female in 4 kHz. There were significant differences in the variables of alcohol drinking (2 kHz) and headset volume (8 kHz) in both ears. While this study does not show any significant factors that affect the hearing thresholds in the occupational characteristics, the gender and the previous ear related diseases, non-occupational characteristics, were found as the factors in 4 kHz. It is suggested that the more detailed survey be performed to identify the occupational factors that affect the hearing thresholds in the call center workers based on the result derived from this study.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.891-899
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• 2010

Association rule is the technique to represent the relationship between two or more items by numerical representing for the relevance of each item in vast amounts of databases, and is most being used in data mining. The basic thresholds for association rule are support, confidence, and lift. these are used to generate the association rules. We need standardization of lift because the range of lift value is different from that of support and confidence. And also we need standardization of support and confidence to compare objectively association level of antecedent variables for one descendant variable. In this paper we propose a method for standardization of association thresholds considering marginal probability for each item to grasp objectively and exactly association level, check the conditions for association criteria and then compare association thresholds with standardized association thresholds using some concrete examples.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.579-586
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• 2011

It is very critical for image processing of IIR (Imaging Infrared) seekers to achieve improved guidance performance for missile systems to determine appropriate thresholds in various environments. In this paper, we propose automatic threshold determination methods for proper thresholds to extract definite target signals in an EOCM (Electro-Optical Countermeasures) environment with low SNR (Signal-to-Noise Ratios). In particular, thresholds are found to be too low to extract target signals if one uses the Otsu method so that we suggest a Shifted Otsu method to solve this problem. Also we improve extracting target signal by changing Shifted Otsu thresholds according to the TBR (Target to Background Ratio). The suggested method is tested for real IIR images and the results are compared with the Otsu method. The HPDAF (Highest Probabilistic Data Association Filter) which selects the target originated measurements by taking into account of both signal intensity and statistical distance information is applied in this study.

• Journal of Nutrition and Health
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• v.49 no.2
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• pp.88-98
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• 2016

Purpose: We recruited 118 women in their early 20's to examine the relationship between sodium intake and salty taste thresholds and preference. We also examined the association of salty taste preference with sodium-related dietary behaviors and major dishes contributing to sodium intake. Methods: Daily sodium intake was estimated using a 127-item dish-frequency questionnaire. Salty taste thresholds and preference were measured using rating scales using water solution of NaCl and a self-administered questionnaire based on a Likert scale, respectively. Results: Salty taste preference showed positive correlation with daily sodium intake and sodium intake-increasing behaviors, and inverse association with sodium intake-decreasing behaviors, including salt and soy sauce use at the table, the frequency of eating out and home delivery of foods, broth consumption of soup, stew or noodle soup, the use of ready-to-serve or processed foods, fresh vegetable intake, and the accommodating attitude toward bland food. Intake of sodium-contributing dishes, including ramen, spicy soft-tofu stew, radish kimchi, and dishes containing kimchi, also showed positive association with salty taste preference. Unexpectedly, detection and recognition thresholds of salty taste showed no association with salty taste preference, sodium intake, and sodium-related dietary behaviors. Conclusion: These findings suggest that salty taste preference could reflect sodium intake of individuals rather than thresholds of saltiness, and may be used as a simple and effective proxy for usual sodium intake.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.397-405
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• 2010

Data mining is the method to find useful information for large amounts of data in database, and one of the important goals is to search and decide the association for several variables. The task of association rule mining is to find certain association relationships among a set of data items in a database. There are three primary measures for association rule, support and confidence and lift. In this paper we developed a association rule ranking function by decreased lift influence to generate association rule for items satisfying at least one of three criteria. We compared our function with the functions suggested by Park (2010), and Wu et al. (2004) using some numerical examples. As the result, we knew that our decision function was better than the function of Park's and Wu's functions because our function had a value between -1 and 1regardless of the range for three association thresholds. Our function had the value of 1 if all of three association measures were greater than their thresholds and had the value of -1 if all of three measures were smaller than the thresholds.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.495-503
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• 2011

The most widely used data mining technique is to find association rules. Association rule mining is the method to quantify the relationship between each set of items in very huge database based on the association thresholds. There are some basic association thresholds to explore meaningful association rules ; support, confidence, lift, etc. Among them, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The net confidence and the attributably pure confidence were developed to compensate for this drawback, but they have other drawbacks.In this paper we consider some predictive similarity measures for binary data in cluster analysis and multi-dimensional analysis as association threshold to compensate for these drawbacks. The comparative studies with net confidence, attributably pure confidence, and some predictive similarity measures are shown by numerical example.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1113-1121
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• 2011

Recently, a variety of data mining techniques has been applied in various fields like healthcare, insurance, and internet shopping mall. Association rule mining is a popular and well researched method for discovering interesting relations among large set of data items. Association rule mining is the method to quantify the relationship between each set of items in very huge database based on the association thresholds. There are three primary quality measures for association rules; support and confidence and lift. In this paper we consider some similarity measures with negative co-occurrence frequencies which is widely used in cluster analysis or multi-dimensional analysis as association thresholds. The comparative studies with support, confidence and some similarity measures are shown by numerical example.