• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.55-75
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• 2010

Sequential pattern mining aims to discover interesting sequential patterns in a sequence database, and it is one of the essential data mining tasks widely used in various application fields such as Web access pattern analysis, customer purchase pattern analysis, and DNA sequence analysis. In general sequential pattern mining, only the generation order of data element in a sequence is considered, so that it can easily find simple sequential patterns, but has a limit to find more interesting sequential patterns being widely used in real world applications. One of the essential research topics to compensate the limit is a topic of weighted sequential pattern mining. In weighted sequential pattern mining, not only the generation order of data element but also its weight is considered to get more interesting sequential patterns. In recent, data has been increasingly taking the form of continuous data streams rather than finite stored data sets in various application fields, the database research community has begun focusing its attention on processing over data streams. The data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. In data stream processing, each data element should be examined at most once to analyze the data stream, and the memory usage for data stream analysis should be restricted finitely although new data elements are continuously generated in a data stream. Moreover, newly generated data elements should be processed as fast as possible to produce the up-to-date analysis result of a data stream, so that it can be instantly utilized upon request. To satisfy these requirements, data stream processing sacrifices the correctness of its analysis result by allowing some error. Considering the changes in the form of data generated in real world application fields, many researches have been actively performed to find various kinds of knowledge embedded in data streams. They mainly focus on efficient mining of frequent itemsets and sequential patterns over data streams, which have been proven to be useful in conventional data mining for a finite data set. In addition, mining algorithms have also been proposed to efficiently reflect the changes of data streams over time into their mining results. However, they have been targeting on finding naively interesting patterns such as frequent patterns and simple sequential patterns, which are found intuitively, taking no interest in mining novel interesting patterns that express the characteristics of target data streams better. Therefore, it can be a valuable research topic in the field of mining data streams to define novel interesting patterns and develop a mining method finding the novel patterns, which will be effectively used to analyze recent data streams. This paper proposes a gap-based weighting approach for a sequential pattern and amining method of weighted sequential patterns over sequence data streams via the weighting approach. A gap-based weight of a sequential pattern can be computed from the gaps of data elements in the sequential pattern without any pre-defined weight information. That is, in the approach, the gaps of data elements in each sequential pattern as well as their generation orders are used to get the weight of the sequential pattern, therefore it can help to get more interesting and useful sequential patterns. Recently most of computer application fields generate data as a form of data streams rather than a finite data set. Considering the change of data, the proposed method is mainly focus on sequence data streams.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.377-385
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• 2009

Twenty five female students in Seoul participated and were divided into two group in the experiment of weight loss effect of two treatments. Fourteen students(Treatment A group), randomly chosen from the students, had fed on diet foods and exercised over 8 weeks, and the remaining students(Treatment B group) had fed on diet foods only for the same periods. Weights of 25 students had been measured repeatedly four times at an interval of two weeks during 8 weeks, It resulted from mixed model analysis of repeated measurements data that separate Toeplitz pattern for each treatment group was selected as the optimal covariance pattern. Based upon the optimal covariance pattern model, the baseline effect and time effect were found to be highly significant, but the treatment-time interaction effect was found to be insignificant. Finally, the students with diet foods and exercises were more effective in losing weight than the students with only diet foods were.

• Korean Journal of Food Science and Technology
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• v.21 no.3
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• pp.345-350
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• 1989

Saprophagous fungi which were isolated from ginseng products were investigated the change of mycellial weight, saponin pattern and saponin contents according to culture periods at different of saponin concentration. Aspergillus sp. showed the greatest mycellial weight in 9 days at 0.3% saponin concentration as well as Penicillium specise A and B. Mycellial weight of all Saprophagous fungi was decresed than control group at 1.0% concentration of crude saponin. Saponin pattern were changed in 6th days of culture by Aspergillus sp. at 0.3% and deteriorated diol ginsenoside respectively. The amount of diol saponins was decreased all the duration of culture by Aspergillus sp. and Penicillium sp. B. whereas Pencillium sp. A was not any change. The amont of saponin in the fresh ginseng and white ginseng medium was decreased gradually according to culture periods by the saprophagous fungi.

• Journal of Nutrition and Health
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• v.32 no.7
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• pp.767-774
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• 1999

This study was carrid out to examine a part of the mechanism for the etiology of diabetic complications. Thirty normal and forty streptozotocin(STZ)-induced diabetic rats were used as the animal models. Animals were sacrificed at the time points of 3 days, 1, 2, 4 and 6 weeks after STZ-injection and time course in body weight and organ weight, the levels of blood glucose, plasma lipid patterns, and atherogenic index were measured during 6 weeks. The STZ-diabetic animals showed 63% survival rate and fsting blood glucose levels of the diabetic animals measured in the range of 230-410mg/dL during the experimental period. The body weigh of diabetic animals decreased significantly throughout the experimental period and the relative weights of organs to body weight were significantly higher than the normal control ones. The enlargement of the kidney in the diabetic animals was especially remarkable. Plasma triglyceride concentration in diabetic rats substancially increased from the first week of onset of diabetes mellitus and maintained higher levels than the control ones throughout the whole experimental period. The plasma total cholesterol level and atherogenic index in the diabetic rats were significantly higher than the normal ones from the third day after STZ injection and showed a gradual increase with the duration of the disease. Throughout the experiment, the diabetic rats consistently showed a slightly lower HDL-cholesterol level compared to the normal animals. From the results of this study, it appears that the significant changes in blood lipid pattern in STZ-diabetic animals start from the first week after STZ injection.

• Journal of Digital Contents Society
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• v.17 no.2
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• pp.81-88
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• 2016

The general frequent pattern mining considers frequency and support of items. To extract useful information, it is necessary to consider frequency and weight of items that reflects the changing of user interest as time passes. The suitable services considering time or location is requested by user so that the weighted mining method is necessary. We propose a method of weighted frequent pattern mining based on service ontology. The weight considering time and location is given to service items and it is applied to association rule mining method. The extracted rule is combined with stored service rule and it is based on timely service to offer for user.

• Journal of the Korea Society of Computer and Information
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• v.21 no.2
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• pp.105-112
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• 2016

In this paper, we propose a technique that can be mapped to the most appropriate type of health patterns, depending on the health status of health promotion measures to establish a body mass index (BMI). When used as a mapping scheme proposed in this paper it is possible to contribute to effective healthcare and health promotion. BMI is widely used as a simple way to assess obesity because body fat increases the status and relevance. Despite normal weight determined by this and because of the social atmosphere has increased prefer the skinny tend to try to excessive weight loss. Since health can affect the health maintenance and promotion of the rest of your life, depending on whether and how much weight perception and health can be considered as very important. Therefore, this paper identifies the differences in perception and in this respect for the body mass index (BMI). And physical, mental and map the appropriate type of pattern in the relationship between body mass index (BMI) in order to facilitate the social and health conditions. Proposal to give such a mapping technique provides the opportunity to increase the efficiency of health care and health promotion.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.95-104
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• 1993

IPA(interpattern association) model is a method of feature extraction using a neural network. Even in the case that the reference patterns are simuklar to one another, this model can recover the reference patterns effectively. However, when the pattern whose feature pixels are lost is used as input, this model can not guarantee perfect recovery of the reference pattern. It is proposed a improved interpattern association(IPA) model for the feature extraction using neural network. The improved IPA model that combines the first interconnection weight matrix of the IPA model with the second additional weight matrix is proposed here to overcome the recovery problem of the original IPA model. The results of computer simulation and optical experiment are advanced.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.10
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• pp.446-452
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• 2002

For most of pattern recognition applications, it is required to correctly recognize patterns even if they have translation variations. In this paper, to achieve the goal of translation invariant pattern recognition, we propose a new generalized translation invariant second-order neural network using a constraint on the weights. The weight constraint is implemented using generalized translation invariant features which are accumulated sums of pixel combinations. Simulation results will be given to demonstrate that the proposed second-order neural network has the generalized translation invariant property.

• Transactions of Materials Processing
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• v.33 no.2
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• pp.123-131
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• 2024

Plastic, the main material of FFF-type 3D printing, exhibits lower strength compared to metal. research aimed at increasing strength is needed for use in various industrial fields. This study analyzed three X-shape infill patterns(grid, lines, zigzag) with similar internal lattice structure. Moreover, tensile test considering weight and printing time was conducted based on the infill line multiplier and infill overlap percentage. The three X-shape infill patterns(grid, lines, zigzag) showed differences in nozzle paths, material usage and printing time. When infill line multiplier increased, there was a proportional increase in tensile strength/weight and tensile strength/printing time. In terms of infill overlap percentage, the grid pattern at 50% and the zigzag and lines patterns at 75% demonstrated the most efficient performance.

• Journal of the Korean Society of Food Culture
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• v.20 no.2
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• pp.172-185
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• 2005

This study was carried out to investigate the information concerning dietary patterns and analysis of the various factors that influence obesity. The subjects of this study were 1,020 middle and high school students in Seoul. Subjects were classified into under weight, normal weight and over weight group by body mass index. We investigated eating habits, life habits, food behavior and food consumption. Data were collected by questionnair and analysed with the SAS program. The results of this study way are summarized and concluded as fellows; In the case of dietary pattern, over weight group showed significantly higher in skipping a meal than the other group. Also over weight group tend to eat fast. There were significant differences of food intake frequency score by body mass index. From the results of factor analysis of variable related to obesity, 4 factors were generated and the factors were named 'Food behavior related to obesity', 'Snack consumption pattern', 'Life habit', 'Family environment related to food habit'. These factors were associated with obesity. To maintain nutritional balance and health, we should implement to ensure good dietary patterns.