• 정보과학회 컴퓨팅의 실제 논문지
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• 제20권12호
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• pp.711-716
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• 2014

최근 논문 표절 논란과 지능형 텍스트 검색서비스에 대한 관심이 증가하면서 문장 유사도 측정의 필요성이 증가하고 있다. n-gram, 편집거리, LSA 등 기존의 다양한 방향으로 선행 연구가 있었지만 각 기법마다 장단점이 존재한다. 본 논문에서는 집합 기반 POI 검색 기법을 이용한 새로운 방향의 문장 유사도 측정 기법을 제안한다. 집합 기반 POI 검색 기법은 하드매칭에 비해 단어의 도치, 누락, 삽입, 변경에 현저한 성능 향상을 보인다. 이 기법을 이용하면 보다 정확하고 빠른 문장 유사도 측정이 가능하다. 제안하는 기법은 기존 집합 기반 POI 검색 기법의 데이터 로딩 알고리즘과 텍스트 검색 알고리즘을 변형하고 어절 연산 알고리즘을 추가하여 두 문장의 유사도를 백분율로 표현한다. 실험을 통해 본 논문에서 제시하는 기법이 정확도와 속도에서 n-gram과 기존 집합 기반 POI 검색 기법에 비해 우수함을 확인하였다.

• ETRI Journal
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• 제38권3호
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• pp.540-550
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• 2016

Spectral clustering is a powerful tool for exploratory data analysis. Many existing spectral clustering algorithms typically measure the similarity by using a Gaussian kernel function or an undirected k-nearest neighbor (kNN) graph, which cannot reveal the real clusters when the data are not well separated. In this paper, to improve the spectral clustering, we consider a robust similarity measure based on the shared nearest neighbors in a directed kNN graph. We propose two novel algorithms for spectral clustering: one based on the number of shared nearest neighbors, and one based on their closeness. The proposed algorithms are able to explore the underlying similarity relationships between data points, and are robust to datasets that are not well separated. Moreover, the proposed algorithms have only one parameter, k. We evaluated the proposed algorithms using synthetic and real-world datasets. The experimental results demonstrate that the proposed algorithms not only achieve a good level of performance, they also outperform the traditional spectral clustering algorithms.

• Smart Structures and Systems
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• 제23권3호
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

• International journal of advanced smart convergence
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• 제5권2호
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• pp.53-58
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• 2016

With the growing use of big data and data mining, it serves to understand how such techniques can be used to understand various relationships in the healthcare field. This study uses hierarchical methods of data analysis to explore similarities in hospitalization across several New York state counties. The study utilized methods of measuring crowding distance of data for age-specific hospitalization period. Crowding distance is defined as the longest distance, or least similarity, between urban cities. It is expected that the city of Clinton have the greatest distance, while Albany the other cities are closer because they are connected by the shortest distance to each step. Similarities were stronger across hospital stays categorized by age. Hierarchical clustering can be applied to predict the similarity of data across the 10 cities of hospitalization with the measurement of crowding distance. In order to enhance the performance of hierarchical clustering, comparison can be made across congestion distance when crowding distance is applied first through the application of converting text to an attribute vector. Measurements of similarity between two objects are dependent on the measurement method used in clustering but is distinguished from the similarity of the distance; where the smaller the distance value the more similar two things are to one other. By applying this specific technique, it is found that the distance between crowding is reduced consistently in relationship to similarity between the data increases to enhance the performance of the experiments through the application of special techniques. Furthermore, through the similarity by city hospitalization period, when the construction of hospital wards in cities, by referring to results of experiments, or predict possible will land to the extent of the size of the hospital facilities hospital stay is expected to be useful in efficiently managing the patient in a similar area.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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• pp.100-104
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• 2006

Fuzzy techniques can be applied in many domains of computer vision community. The definition of an adequate similarity measure for measuring the similarity between fuzzy sets is of great importance in the field of image processing, image retrieval and pattern recognition. This paper proposes a new class of the similarity measures. The properties, sensitivity and effectiveness of the proposed measures are investigated and tested on real data. Experimental results show that these similarity measures can provide a useful way for measuring the similarity between fuzzy sets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2527-2545
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• 2016

Measuring the similarity of given samples is a key problem of recognition, clustering, retrieval and related applications. A number of works, e.g. kernel method and metric learning, have been contributed to this problem. The challenge of similarity learning is to find a similarity robust to intra-class variance and simultaneously selective to inter-class characteristic. We observed that, the similarity measure can be improved if the data distribution and hidden semantic information are exploited in a more sophisticated way. In this paper, we propose a similarity learning approach for retrieval and recognition. The approach, termed as LDA-FEK, derives free energy kernel (FEK) from Latent Dirichlet Allocation (LDA). First, it trains LDA and constructs kernel using the parameters and variables of the trained model. Then, the unknown kernel parameters are learned by a discriminative learning approach. The main contributions of the proposed method are twofold: (1) the method is computationally efficient and scalable since the parameters in kernel are determined in a staged way; (2) the method exploits data distribution and semantic level hidden information by means of LDA. To evaluate the performance of LDA-FEK, we apply it for image retrieval over two data sets and for text categorization on four popular data sets. The results show the competitive performance of our method.

• 한국콘텐츠학회논문지
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• 제9권2호
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• pp.43-50
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• 2009

본 연구에서는 고유의 비디오 프레임의 특징 데이터를 추출하고 추출된 특징 데이터를 1차 신호로 생성하여 유사한 비디오 프레임 데이터를 검출하는 방법에 관하여 연구하였다. 비디오 간의 유사도 검출을 위하여 유사한 프레임간의 경계를 얻어낸 후 경계 범위 내에서 대표 프레임을 얻어낸다. 생성된 대표 프레임으로부터 blurring 된 프레임을 생성하고, DOG 값을 이용하여 특징 데이터를 추출한다. 이렇게 생성된 특징 데이터를 1차원 신호로 나열하고 콘텐츠 간 유사도를 비교한다. 실험 결과 잡음 첨가, 회전 변환, 크기 변환, 프레임 절삭, 프레임 제거 공격에 대해서도 유사도 수치 0.9 이상의 매우 강인한 특성을 나타냈다.

• 한국정보기술학회논문지
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• 제17권12호
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• pp.29-38
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• 2019

라이프로그를 이용한 경로 예측 기법은 정확한 경로 예측을 위하여 많은 양의 학습 데이터를 요구하며, 학습 데이터가 부족할 경우 경로 예측 성능이 저하된다. 학습 데이터 부족은 사용자의 이동 패턴이 유사한 다른 사용자의 데이터를 이용하여 해결이 가능하다. 따라서 이 논문은 사용자 유사도 기반 경로 예측 알고리즘을 제안한다. 이를 위하여 제안 알고리즘은 경로를 3단 그리드 패턴으로 학습하고 코사인 유사도 기법을 이용하여 사용자 간 유사도를 측정한다. 이후, 측정된 유사도를 학습된 모델에 적용하여 경로를 예측한다. 평가를 위하여 기존 경로 예측 기법들과 제안 기법의 경로 예측 정확도를 측정 및 비교한다. 그 결과, 제안 기법의 정확도는 66.6%로 다른 기법들에 비해 평균 1.8% 더 높은 정확도를 가진 것으로 평가된다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2006년도 춘계학술발표회 논문집
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• pp.243-248
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• 2006

In this paper, we propose the unsupervised change detection algorithm that apply the similarity measure techniques to the hyperspectral image. The general similarity measures including euclidean distance and spectral angle were compared. The spectral similarity scale algorithm for reducing the problems of those techniques was studied and tested with Hyperion data. The thresholds for detecting the change area were estimated through EM(Expectation-Maximization) algorithm. The experimental result shows that the similarity measure techniques and EM algorithm can be applied effectively for the unsupervised change detection of the hyperspectral data.

• 한국지능시스템학회논문지
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• 제11권7호
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• pp.579-583
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• 2001

In the area of data clustering in high dimensional space, one of the difficulties is the time-consuming process for computing vector similarities. It becomes worse in the case of the agglomerative algorithm with the group-average link and mean centroid method, because the cluster similarity must be recomputed whenever the cluster center moves after the merging step. As a solution of this problem, we present an incremental method of similarity computation, which substitutes the scalar calculation for the time-consuming calculation of vector similarity with several measures such as the squared distance, inner product, cosine, and minimum variance. Experimental results show that it makes clustering speed significantly fast for very high dimensional data.