• The Journal of Information Technology
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• v.9 no.3
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• pp.27-32
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• 2006

If is interested and create rule between it in item that association rules buys, by negative association rules is interested to item that do not buy, it is attempt to do data Maining more effectively. It is difficult that existent methods to find negative association rules find one part of rule, or negative association rules because use more complicated algorithm than algorithm that find association rules. Therefore, this paper presents method to create negative association rules by simpler process using Boolean Analyzer that use dependency between items. And as Boolean Analyzer through an experiment, show that can find negative association rules and more various rule through comparison with other algorithm.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.569-576
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• 2008

Association rule mining searches for interesting relationships among items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. Association rule is an interesting rule among purchased items in transaction, but the negative association rule is an interesting rule that includes items which are not purchased. Boolean Analyzer is the method to produce the negative association rule using PIM. But, PIM is subjective. In this paper, we present statistical objective criterion in negative association rules using Boolean Analyzer.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.145-151
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• 2006

Association rule mining searches for interesting relationships among items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule support and confidence and lift. Association rule is an interesting rule among purchased items in transaction, but the negative association rule is an interesting rule that includes items which are not purchased. Boolean Analyzer is the method to produce the negative association rule using PIM. But PIM is subjective. In this paper, we present statistical objective criterion in negative association rules using Boolean Analyzer.

• Human Ecology Research
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• v.53 no.4
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• pp.405-412
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• 2015

This study investigated the moderating effect of negative emotionality on the association between teacher-child intimacy and peer interaction based on a differential susceptibility model. The participants were 252 three-year-old children recruited from a day care center and preschool located in Incheon and Gyeonggi province. The teacher-child relationship was measured on a Student-Teacher Relationship Scale. This measure is a type of teacher's report with ratings based on a teacher's daily observations. This scale is composed of closeness items on the degree of warmth and open communication in teacher-child relationships. Peer interactions were measured with a Penn Interactive Peer Play Scale. This measure is composed of play interaction items, play disruption items and play disconnection. Negative emotionality was measured with Child Behavior Questionnaire. Teachers measured teacher-child intimacy and peer interaction scales. Parents reported children's negative emotionality. The research showed that negative emotionality moderated the association of teacher-child intimacy, play interaction, play isolation and play disruption. The magnitude of association between teacher-child intimacy and play disconnection as well as play interaction was greater for high levels of negative emotionality. Teacher-child intimacy was significantly associated with play disruption only for high levels of negative emotionality. The findings of this study support a differential susceptibility model.

• Journal of Families and Better Life
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• v.32 no.3
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• pp.147-156
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• 2014

The purpose of this study was to investigate the moderating effect of negative emotionality on the association between mother-child intimacy and peer acceptance based on differential susceptibility model. According to differential susceptibility model, negative emotionality and difficult temperament is potential differential susceptibility factors. The participants were 3-year-old children recruited from preschools and daycare centers. Teachers completed measurement of negative emotionality. Peer acceptance was measured by peer nomination. Mother-child intimacy was reported by mothers. The results presented that negative emotionality significantly moderated the relation between mother-child intimacy and peer acceptance only for boys. For boys with high level of negative emotionality, mother-child intimacy significantly predicted peer acception. However, for boys with low level of negative emotionality, mother-child intimacy did not associate with peer acception. For girls, negative emotionality did not significantly influence the association between mother-child intimacy and peer acceptance. These findings support differential susceptibility hypothesis that vulnerable children are susceptible to positive parenting effects.

• Journal of the Chungcheong Mathematical Society
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• v.26 no.1
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• pp.137-145
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• 2013

In this paper we obtain the complete convergence of weighted sums of asymptotically almost negatively associated random variables. Some previous known results for negatively associated random variables are generalized to asymptotically almost negative association case.

• Journal of the Korean Home Economics Association
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• v.44 no.11
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• pp.23-34
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• 2006

The purpose of this study was to examine: (1) how mothers generally react to children's negative emotions; (2) whether there were relationships between children's temperament and mothers' reactions to children's negative emotions; (3) whether there were relationships between mothers' depression and parenting stress, and their reactions to children's negative emotions; (4) whether there were relationships between mothers' reactions to children's negative emotions and children's behavior problems; and (5) whether there were relationships between mothers' reactions to children's negative emotions and children's social competence. The subjects were 246 preschoolers and their mothers. It was found that emotion-focused socialization and problem-focused socialization were the most frequently used reactions by mothers to cope with children's negative emotions. Relationships between children's temperament and mothers' reactions to children's negative emotions, those between mothers' depression and parenting stress and their reactions to children's negative emotions, those between mothers' reactions to children's negative emotions and children's behavior problems, and those between mothers' reactions to children's negative emotions and children's social competency were all partially significant. Sex differences were found in some of the relationships found significant.

• Journal of the Korea Fashion and Costume Design Association
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• v.8 no.1
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• pp.13-24
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• 2006

The college students selected by random sampling were classified into four groups by their body cathexis and clothing attitude to investigate differences in the consumer's Impulse-Buying in a negative consumer situation. Consumer cluster were classified into four groups: positive congruity(G1), positive incongruity(G2), negative congruity(G3), negative incongruity(G4). The result are as follows: G1 had high impulse. After Impulse-buying in a negative consumer situation they had negative attitude. G2 tends to do Impulse-buying a lot in a negative consumer situation. G3 had low impulse. After impulse-buying in a negative consumer situation they had both positive and negative attitude. After impulse-buying, G4 had negative and they tend to do a pure impulse buying in a negative consumer situation. As a result, the buying-impulse could cause the impulse-buying.

• Journal of the Korean Home Economics Association
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• v.42 no.1
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• pp.197-206
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• 2004

The purpose of this study was to investigate mothers' reactions to their children's negative emotions. The subject were 145 children(three to six-year-olds) and the instruments were Coping with Children's Negative Emotions Scale by Eisenberg(1992) and Temperament Rating Scale by Chun(1992). The main results were as follows； First, mothers, in general, used more positive reactions than negative reactions when their children showed negative emotions. Mothers' education, job, family type and income affected mothers' type of reactions to their children's negative emotions. Mothers' reactions to their children's negative reaction emotions were associated with their children's temperament but not with their children's age, sex and birth order.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.8 no.1
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• pp.51-68
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• 2001

The purpose of this study was to explain the relationship between smoking status, smoking behavior and hardiness in university students in Gangnung City. The subjects were a convenience sample of 315 students. The data were collected by a questionnaire given to the students between May 22 to June 2, 2000. An instrument developed by Akers & Gang(1996) and translated by Sohn, Jung-Nam(1999) was used in this study to measure definition of smoking, differential reinforcement of smoking and smoking behavior. The differential peer association scale developed Krohn et al.(1982) and translated by Sohn, Jung-Nam(1999), and the hardiness scale developed by Pollock(1984) and translated by Suh, Mun-Sa(1988) were also used. The data were analyzed using the SAS/PC+ Program and included descriptive statistics, t-test, ANOVA, and Spearman correlation coefficients. The results of this study are as follows : 1. The smoking rate for university students was 50.5% of which 44.7% started smoking in high school. 2. The mean score for level of hardiness was $3.14{\pm}0.43$ 3. The mean score for smoking behavior was as follows : 1) The mean score for the neutralizing definition was $2.16{\pm}0.57$ 2) The mean score for the negative definition was $2.37{\pm}0.71$ 3) The mean score for the positive differential reinforcement was $1.89{\pm}0.63$ 4) The mean score for the negative differential reinforcement was $2.96{\pm}0.64$ 5) The mean score for the differential peer association was $2.67{\pm}1.05$ 4. The data showed positive correlations between hardiness and the neutralizing definition(r=.1951, P<.001), between hardiness and the positive differential reinforcement(r=.1128, P<.05), between hardiness and the amount of smoking per day(r=.1452, P<.05) between the neutralizing definition and positive differential reinforcement(r=.4212, P<.0001), between the neutralizing definition and differential peer association(r=.1856, P<.001), between the neutralizing definition and age at smoking initiation(r=.1582, P<.05), between the negative definition and negative differential reinforcement(r=.2985, P<.0001), between the positive differential reinforcement and differential peer association(r=.3451, P<.0001), between positive differential reinforcement and the amount of smoking per day(r=.4431, P<.0001), between differential peer association and the duration of smoking(r=.2789, P<.0001), between differential peer association and the amount of smoking per day(r=.5410, P<.0001), between the duration of smoking and the amount of smoking per day(r=.4245, P<.0001). The data showed negative correlations between the neutralizing definition and the negative definition(r=-.2065, P<.001) between the neutralizing definition and negative differential reinforcement(r=-.3943, P<.0001) between the neutralizing definition and duration of smoking(r=-.1957, P<.05), between the negative definition and positive differential reinforcement(r=-.2093, P<.001), between the negative definition and the amount of smoking per day(r=-.2282, P<.001), between positive differential reinforcement and negative differential reinforcement(r=-.5555, P<.0001) between negative differential reinforcement and differential peer association(r=-.3653, P<.0001), between negative differential reinforcement and the amount of smoking per day(r=-.4570, P<.0001), between the age at smoking initiation and the duration of smoking(r=-.4594, P<.0001).