• Journal of the Korean Society of Environmental Restoration Technology
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• v.14 no.2
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• pp.1-10
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• 2011

Six pine trees (Pinus densiflora S. et Z.) at the Euilimji Lake Park in Jecheon were collected to investigate tree ages, growth decline pattern and the years of death. Tree-ring measurement was carried out using the Lintab with a resolution of 0.01mm. Tree age were 80-176 years. Cross-dating between the tree-ring series of each tree and the local chronology from Worak Mountain resulted that four and two trees died in 1998 and 1999, respectively. Three dead trees had only formed earlywood in the outermost tree ring and the others had incomplete latewood. Therefore, it was proven that the former trees died between spring and early summer, whereas the later ones died during late summer and/or autumn. The simultaneous deaths of trees suggest the insect damage and/or drought may be the crucial reason of the death, but frequent reaction woods, which were formed by leaning stem, and scars formed by physical damage may also contribute to the death.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.275-278
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• 2008

Recently, there are many research of datamining. On the transaction dataset, association rules is made by finding of interesting patterns. A part of mining, sub-structure mining is increased in interest of and applied to many high technology. But graph mining has more computing time then itemset mining. Therefore, that need efficient way for avoid duplication. GASTON is best algorithm of duplication free. This paper analyze GASTON and expect the future work.

• The Journal of the Korea institute of electronic communication sciences
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• v.2 no.2
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• pp.99-105
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• 2007

MANET(Mobile Ad-hoc network) have recently attracted a lot of attention in the research community as well as in industry. Although the previous research mainly focused on various of MANET in routing, we consider, in this paper, how to efficiently support applications such as variable geocasting basd on MANET. The goal of a geocasting protocol is deliver data packet to a group of nodes that are located within a specified geocasting region. Previous research that support geocast service in mobilie computing based on MANET have the non-optimization problem of data delivery path, overhead by frequent reconstruction of the geocast tree, and service disruption problem. In this paper, we propose the mobility pattern based geocast technique using variable service range according to the mobility of destination node and resource reservation to solve this problem. The experimental results show that our proposed mechanism has improved performance of Accessibility & Network Overhead than previous research.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.19-25
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• 2020

In frequent pattern mining, the uncertainty of each item is accompanied by a loss of information. AAlso, in real environment, the importance of patterns changes with time, so fuzzy logic must be applied to meet these requirements and the dynamic characteristics of the importance of patterns should be considered. In this paper, we propose a fuzzy utility mining technique for extracting frequent web page sets from web log databases through fuzzy utility-based web page set mining. Here, the downward closure characteristic of the fuzzy set is applied to remove a large space by the minimum fuzzy utility threshold (MFUT)and the user-defined percentile(UDP). Extensive performance analyses show that our algorithm is very efficient and scalable for Fuzzy Utility Mining using dynamic weights.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.155-162
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• 2008

Biological sequences such as DNA sequences and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological dataset with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with the fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. As the result, the experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.19-20
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• 2017

최근 소셜 미디어와 센서 장비의 기술의 발달로 그래프 데이터의 양이 급격히 증가 하였다. 그래프 데이터의 처리 과정에서 I/O 비용이 발생하여 데이터가 많아지면 병목현상으로 인해 데이터의 처리와 관리에 있어 성능에 한계가 발생한다. 이러한 문제를 해결하기 위해 데이터를 메모리에서 관리하는 캐시 기법에 대한 연구가 이루어 졌다. 본 논문에서는 서브그래프 데이터의 접근 패턴을 고려한 캐싱 기법을 제안한다. 그래프 환경에서 그래프 질의 이력을 통해 패턴을 찾고 질의 관리 테이블과 FP(frequent pattern)-Tree 통해 선별된 데이터를 메모리에 적재시킨다. 또한, 캐시 실패(cache miss)가 발생 하였을 때, 주변의 이웃 정점을 같이 메모리에 적재시킨다. 메모리가 가득 찰 경우 캐시 된 데이터를 퇴출시키는 교체 전략을 제안한다.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• Ecology and Resilient Infrastructure
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• v.9 no.2
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• pp.83-92
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• 2022

The 2020 summer extreme flood severely disturbed the riparian ecosystem of the Seomjin River. Some trees were killed by the flood impact, whereas others have recovered through epicormic regeneration after the disturbance. At the same time, several tree individuals newly germinated. This research aimed to explain the recovery of the riparian ecosystem by spatial proximity between each tree individual of different characteristics, such as "dead", "recovered", and "newly germinated". A spatial point pattern analysis based on K and g-functions revealed that the newly germinated trees and the existing trees were distributed in the spatially clumping patterns. However, further detailed analysis revealed that the new trees were statistically less attracted to the recovered trees than the dead trees, implying competitive interactions hidden in the facilitative interactions. Habitat amelioration by the existing trees positively affected the growth of the new trees, while "living" existing trees were competing with the new trees for resources. This research is expected to provide new knowledge in this era of rapid climate change, which likely induces stronger and more frequent natural disturbance than before. Environmental factors have been widely used for ecosystem modeling, but species interactions, represented by the relative spatial distribution of plant individuals, are also valuable factors explaining ecosystem dynamics.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.187-198
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• 2004

The World-Wide Web is the largest distributed Information space and has grown to encompass diverse information resources. However, although Web is growing exponentially, the individual's capacity to read and digest contents is essentially fixed. From the view point of Web users, they can be confused by explosion of Web information, by constantly changing Web environments, and by lack of understanding needs of Web users. In these Web environments, mining traversal patterns is an important problem in Web mining with a host of application domains including system design and Information services. Conventional traversal pattern mining systems use the inter-pages association in sessions with only a very restricted mechanism (based on vector or matrix) for generating frequent k-Pagesets. We develop a family of novel algorithms (termed WebPR - Web Page Recommend) for mining frequent traversal patterns and then pageset to recommend. Our algorithms provide Web users with new page views, which Include pagesets to recommend, so that users can effectively traverse its Web site. The main distinguishing factors are both a point consistently spanning schemes applying inter-pages association for mining frequent traversal patterns and a point proposing the most efficient tree model. Our experimentation with two real data sets, including Lady Asiana and KBS media server site, clearly validates that our method outperforms conventional methods.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.4
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• pp.465-470
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• 2008

This study describes genetic variability in the BoLA-DRB3 gene in Iranian native cattle (Bos Indicus and Taurus) and relationships between these breeds. This is the first study of genetic polymorphism of the BoLA-DRB3 gene in Iranian native cattle. We examined exon 2 of the major histocompatibility complex (MHC) class II DRB3 gene from 203 individuals in four populations of Iranian native cattle (52 Sarabi, 52 Najdi, 49 Sistani, 50 Golpayegani cattle) using the hemi-nested PCR-RFLP method. We identified the 36 previously reported alleles and one novel pattern (*eac). Analysis of the frequencies of the various BoLA-DRB3.2 alleles in each breed indicated that DRB3.2*52 in Sarabi cattle (23%), DRB3.2 *14 and *24 alleles in Najdi cattle (13%), DRB3.2 *8 allele in Sistani cattle (22%) and DRB3.2*16 allele in Golpayegani cattle (14%), were the most frequent alleles. Allelic frequencies ranged from 1 to 23% among the 36 alleles and there were some alleles that were found only in Iranian cattle. Effective number of alleles in the four breeds was estimated to be 7.86, 11.68, 7.08 and 3.37 in Sarabi, Najdi, Sistani and Golpayegani, respectively. Observed heterozygosities were the highest in Sarabi (94%) and Najdi (94%). A population tree based on the frequency of BoLA-DRB3.2 alleles in each breed suggested that Najdi, Sarabi and Golpayegani cattle clustered together and Najdi and Sarabi were the closest breeds. Sistani cattle differed more from these three breeds. These new data suggest that allele frequencies differ between Iranian cattle breeds.