• 산업경영시스템학회지
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• 제45권3호
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• pp.123-130
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• 2022

Pair trading is a statistical arbitrage investment strategy. Traditionally, cointegration has been utilized in the pair exploring step to discover a pair with a similar price movement. Recently, the clustering analysis has attracted many researchers' attention, replacing the cointegration method. This study tests a clustering-driven pair trading investment strategy in the Korean stock market. If a pair detected through clustering has a large spread during the spread exploring period, the pair is included in the portfolio for backtesting. The profitability of the clustering-driven pair trading strategies is investigated based on various profitability measures such as the distribution of returns, cumulative returns, profitability by period, and sensitivity analysis on different parameters. The backtesting results show that the pair trading investment strategy is valid in the Korean stock market. More interestingly, the clustering-driven portfolio investments show higher performance compared to benchmarks. Note that the hierarchical clustering shows the best portfolio performance.

• 대한전기학회논문지:전력기술부문A
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• 제55권10호
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• pp.410-417
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• 2006

Generally, the training set of maximum $m{\times}L(m+f)$ patterns in the pattern recognition method is required for the real-time bus reconfiguration strategy when a main transformer fault occurs in the distribution substation. Accordingly, to make the application of pattern recognition method possible, the size of the training set must be reduced as efficient level. This Paper proposes a methodology which obtains the minimized training set by applying the pattern clustering method to load patterns of the main transformers and feeders during selected period and to obtain bus reconfiguration strategy based on it. The MaxMin distance clustering algorithm is adopted as the pattern clustering method. The proposed method reduces greatly the number of load patterns to be trained and obtain the satisfactory pattern matching success rate because that it generates the typical pattern clusters by appling the pattern clustering method to load patterns of the main transformers and feeders during selected period. The proposed strategy is designed and implemented in Visual C++ MFC. Finally, availability and accuracy of the proposed methodology and the design is verified from diversity simulation reviews for typical distribution substation.

• 전자공학회논문지C
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• 제35C권7호
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• pp.92-100
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• 1998

In this paper, we suggest a new identification method for sugeno-type fuzzy model via new data clustering strategy. The suggested algorithm is much simpelr than the original identification strategy adopted in. The algorithm suggested in this paper is somewhat similar to that of [2] and [6], that is the algorithm suggested in this paper consists of two steps: coarse tuning and fine tuning. In this paper, double clustering strategy is proposed for coarse tunign. Finally, the resutls of computer simulation are given to demonstrate the validity of this algorithm.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.986-1016
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• 2019

Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.

• 아동학회지
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• 제16권2호
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• pp.125-138
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• 1995

The purpose of the present research was to study developmental trends in categorical organization strategy. The subjects were 160 children - 40 nine - year - old boys, 40 nine - year - old girls, 40 seven - year - old boys, 40 seven - year - old girls. All subjects received one of three lists of items differing in category representativeness in either a free -recall or a sort -recall task. The selection of list materials permitted separation of the effects of age differences in category knowledge from those of knowledge per se on children's recall behavior. The tasks were administered to children individually with the memory task followed by the metamemory task. The data was analyzed with three - way ANOVA arid Pearson's correlation coefficient. The results were that (1) Children's recall, clustering, and metamemory increased with age, while age effects for clustering were restricted to the sort - recall/high typicality condition. At each age level, children showed higher level of recall, clustering and metamemory for category typical rather than atypical list, and sort - recall than free-recall. Level of clustering and metamemory were superior in the sort - recall task and for items of high category typicality. (2) 9 - year - old children were capable of deliberately and efficiently using category organization as a memory strategy at least when appropriate contextual support was present (as determined by task requirements and list materials: sort - recall/high typicality).

• Journal of Information Technology Applications and Management
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• 제23권2호
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• pp.143-152
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• 2016

We can organize, manage, search, and process the documents efficiently by a document clustering. In general, the documents are clustered in a high dimensional feature space because the documents consist of many terms. In this paper, we propose a new method to cluster the documents efficiently in a low dimensional feature space by finding the core concepts from a domain ontology corresponding to the particular area documents. The experiment shows that our clustering method has a good performance.

• 한국컴퓨터정보학회논문지
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• 제16권12호
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• pp.247-255
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• 2011

무선 센서 네트워크에서 클러스터 기반의 계층적 라우팅 프로토콜들은 모든 노드들의 수명을 균등하게 유지하여, 센서 네트워크의 수명을 최대로 연장하는 것을 목표로 하고 있다. 본 논문에서는 싱크의 위치 변화를 고려한 적응적 클러스터링 프로토콜을 제안한다. 본 논문에서 제안하는 클러스터링 프로토콜의 특징은 클러스터 트리의 레벨에 따라 클러스터의 크기를 제한하는 대칭형 계층적 클러스터를 구성함으로써 싱크의 위치 변화에 적응적으로 대응 가능하며, 모든 클러스터의 생존 시간을 향상시킴과 동시에 균등한 생존 시간을 보장할 수 있다. 이 기법의 효율성을 입증하기 위해 기존의 대표적인 클러스터링 프로토콜들인 LEACH, EEUC와 본 논문에서 제안하는 적응적 클러스터링 프로토콜의 에너지 소비 정도를 시뮬레이션을 통해 비교하였으며, 그 결과 에너지 소비와 네트워크 수명의 균형에 대해 더 나은 성능을 얻어낼 수 있었다.

• ETRI Journal
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• 제41권3호
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• pp.326-336
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• 2019

A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a wide-spread research problem. In this paper, we propose a demand-based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to-be-charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K-means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on-demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.406-409
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• 2008

Clustering data streams has an importance over many applications like sensor networks. Existing hierarchical methods follow a semi fuzzy clustering that yields duplicate clusters. In order to solve the problems, we propose an extended online divisive agglomerative clustering on data streams. It builds a tree-like top-down hierarchy of clusters that evolves with data streams using geometric time frame for snapshots. It is an enhancement of the Online Divisive Agglomerative Clustering (ODAC) with a pruning strategy to avoid duplicate clusters. Our main features are providing update time and memory space which is independent of the number of examples on data streams. It can be utilized for clustering sensor data and network monitoring as well as web click streams.

• 한국통신학회논문지
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• 제29권5C호
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• pp.700-705
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• 2004

Autoencoder와 Fuzzy c-Means 알고리즘을 이용하여, 불완전한 데이터의 군집화를 위한 알고리즘이 본 논문에서 제안되었다. 본 논문에서 제안된 Optimal Completion Autoencoder Fuzzy c-Means (OCAEFCM)은 손상되어 불완전한 데이터의 최적 복원과 데이터의 군집화를 위해 Autoencoder Neural Network (AENN) 과 Gradient-based FCM (GBFCM)을 이용하였다. OCAEFCM 의 성능평가를 위해 IRIS 데이터와 금융기관에서 취득한 실제 데이터를 사용하였다 기존의 Optimal Completion Strategy FCM (OCSFCM)과 비교했을 때, 제안된 OCAEFCM 이 OCSFCM 보다 18％-20％의 성능 향상을 보여준다.