• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.17-28
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• 2006

In this paper, we investigate a problem of updating sequential patterns when new transactions are added to a database. We present an efficient updating algorithm for sequential pattern mining that incrementally updates added transactions by reusing frequent patterns found previously. Our performance study shows that this method outperforms both AprioriAll and PrefixSpan algorithm which updates from scratch, since our method can efficiently utilize reduced candidate sets which result from the incremental updating technique.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.13-15
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• 2004

순차 패턴을 찾는 것은 데이타 마이닝 응용분야에서 중요한 문제이다. 기존의 순차 패턴 마이닝 알고리즘들은 아이템으로만 이루어진 순차 패턴만을 취급하였으나 Apriori-QSP에서는 새롭게 퀀터티 정보에 대한 처리의 개념을 도입하였다. 전채 순차 패턴을 찾는 알고리즘들은 너비 우선 탐색과 깊이 우선 탐색 기법으로 분류할 수 있는데, 이러한 분류에서 Apriori-QSP알고리즘은 너비 우선 탐색 기법으로 분류할 수 있다. 본 논문에서는 퀀터티 정보를 처리하는 깊이 우선 탐색 기법을 제안하였다. Apriori-QSP에서 제안되었던 후보패턴 생성에 대한 필터링파 샘플링 기법을 깊이 우선 탐색의 탐색 기법으로 적용하였으며, 다양한 실험 결과들이 깊이 우선 탐색에서도 이러한 기법이 효율적임을 보여 주고 있다. 또한 길이가 긴 순차 패턴 마이닝의 경우 너비우선 탐색에 비해 향상된 성능을 보임을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1365-1368
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• 2012

순차 패턴 탐사 기법은 순서를 갖는 패턴들의 집합 중에 빈발하게 발생하는 패턴을 찾아내는 기법이다. 순차 패턴 탐사 분야 중에 동적 가중치 순차 패턴 탐사는 가중치가 시간에 따라 변화하는 컴퓨팅 환경에 적용하는 마이닝 기법으로 동적인 중요도 변화를 마이닝에 적용하여 다양한 환경에서 활용 가능하다. 이 논문에서는 다양한 순차 데이터에서 동적 가중치를 적용하여 순차 패턴을 탐사하는 새로운 시퀀스 데이터 마이닝 기법에 대하여 제안한다. 제안하는 기법은 시간 순서에 의한 상대적인 동적 가중치를 사용하여 탐색해야 하는 후보 패턴을 줄여줄 수 있어 빈발한 시퀀스 패턴을 빠르게 찾을 수 있다. 이 기법을 사용하면 기존 가중치를 적용하는 방식보다 메모리 사용과 처리 시간을 줄여줘 매우 효율적이다.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.9
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• pp.38-48
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• 1998

Generating test patterns for asynchronous sequential circuits remains to be a very difficult problem. There are few algorithms for this problem, and previous works cut feedback loops, and insert synchronous flip-flops in the feedback loops during ATPG. The conventional algorithms are similar to the algorithms for synchronous sequential circuits. This means that the conventional algorithms generate test patterns by modeling asynchronous sequential circuits as synchronous sequential circuits. So, test patterns generated by those algorithms nay not detect target faults when the test patterns are applied to the asynchronous sequential circuit under test. In this paper an algorithm is presented to generate test patterns for asynchronous sequential circuits. Test patterns generated by the algorithm can detect target faults for asynchronous sequential circuits with the minimal possibility of critical race problem and oscillation. And it is guaranteed that the test patterns generated by the algorithm will detect target faults.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.9
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• pp.1005-1014
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• 2007

In this paper, The four types SRA(Sequential Rotated Array) antennas has been analyzed. Those are consisted of a 4-elements SRA, a 8-elements SRA, the $2{\times}2$ planer array SRA and the triple (4+8+4)-elements SRA. These LHCP SRA antennas are used a probe feeding multi-layer truncated microstrip antenna whose center frequency is 11.85 CHz. The cooperated feeding circuits are designed for feeding to each elements with equal amplitude and regular phase. Comparing with the each simulation results and experiment results of this 4-type SRA antennas, the triple (4+8+4)-elements SRA showed the most electrical characteristics in the degree of integration, high gain and low cross polarization.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.137-144
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• 2013

A sequential pattern mining is finding out frequent patterns from the data set in time order. In this field, a dynamic weighted sequential pattern mining is applied to a computing environment that changes depending on the time and it can be utilized in a variety of environments applying changes of dynamic weight. In this paper, we propose a new sequence data mining method to explore the stream data by applying the dynamic weight. This method reduces the candidate patterns that must be navigated by using the dynamic weight according to the relative time sequence, and it can find out frequent sequence patterns quickly as the data input and output using a hash structure. Using this method reduces the memory usage and processing time more than applying the existing methods. We show the importance of dynamic weighted mining through the comparison of different weighting sequential pattern mining techniques.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.29-36
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• 2009

Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, is an important data mining problem with broad applications. Since a sequential pattern in DNA sequences can be a motif, we studied to find sequential patterns in DNA sequences. Most previously proposed mining algorithms follow the exact matching with a sequential pattern definition. They are not able to work in noisy environments and inaccurate data in practice. Theses problems occurs frequently in DNA sequences which is a biological data. We investigated approximate matching method to deal with those cases. Our idea is based on the observation that all occurrences of a frequent pattern can be classified into groups, which we call approximated pattern. The existing PrefixSpan algorithm can successfully find sequential patterns in a long sequence. We improved the PrefixSpan algorithm to find approximate sequential patterns. The experimental results showed that the number of repeats from the proposed method was 5 times more than that of PrefixSpan when the pattern length is 4.

• The KIPS Transactions:PartD
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• v.18D no.2
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• pp.81-88
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• 2011

Sequential pattern mining that determines frequent patterns appearing in a given set of sequences is an important data mining problem with broad applications. For example, sequential pattern mining can find the web access patterns, customer's purchase patterns and DNA sequences related with specific disease. In this paper, we develop the sequential pattern mining algorithms using MapReduce framework. Our algorithms distribute input data to several machines and find frequent sequential patterns in parallel. With synthetic data sets, we did a comprehensive performance study with varying various parameters. Our experimental results show that linear speed up can be achieved through our algorithms with increasing the number of used machines.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.564-571
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• 1996

The logic synthesis systems for table look up(TLU) type field programmable e gate arrays(FPGAs) have so farstudied mostly the combinational logic problem m. This paper presents for mapping a sequential circuit onto a popular table look up architecture, theXilinx 3090 architecture. In thefirst for solving this problem, combinational and sequential elements which have 6 or7 input combinational and sequential elements which haveless thanor equal to 5 inputs. We heavily use the combinational synthesis techniques tosolve the sequential synthesis problem. Our syntheisis approach is very simple, but its results are reasonable. We compare seveal benchmark Examples with sis-pga(map_together and map_separate) synthesis system and the experimental results show that our synthesis system is 17% betterthan sis-pga sequential synthesis system for TLU PGAs.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.35-46
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• 2010

Sequential pattern mining is one of the essential data mining tasks, and it is widely used to analyze data generated in various application fields such as web-based applications, E-commerce, bioinformatics, and USN environments. Recently data generated in the application fields has been taking the form of continuous data streams rather than finite stored data sets. Considering the changes in the form of data, many researches have been actively performed to efficiently find sequential patterns over data streams. However, conventional researches focus on reducing processing time and memory usage in mining sequential patterns over a target data stream, so that a research on mining more interesting and useful sequential patterns that efficiently reflect the characteristics of the data stream has been attracting no attention. This paper proposes a mining method of sequential patterns over data streams with a gap constraint, which can help to find more interesting sequential patterns over the data streams. First, meanings of the gap for a sequential pattern and gap-constrained sequential patterns are defined, and subsequently a mining method for finding gap-constrained sequential patterns over a data stream is proposed.