• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2008년도 추계학술발표대회
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• pp.336-339
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• 2008

우리가 사용하는 데이터베이스 내에는 많은 양의 데이터 들이 들어 있으며, 계속적으로 그 양은 늘어나고 있다. 이러한 데이터들로부터 질의를 통해 얻을 수 있는 기본적이고 단순한 정보들과 달리 고급 정보를 얻게 해주는 방법이 데이터 마이닝이다. 데이터 마이닝의 기법 중에서 본 논문에서는 k-means 알고리즘을 사용하여 트랜잭션을 클러스터링 함으로써 데이터베이스의 트랜잭션 수를 줄여 연관규칙의 대표적인 알고리즘인 Apriori 알고리즘의 단점인 트랜잭션 스캔으로 인한 성능 저하를 개선하고자 한다.

• Journal of Information Technology Applications and Management
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• 제20권3_spc호
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• pp.219-230
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• 2013

Real time accessiblity and agility in Ubiquitous-commerce is required under ubiquitous computing environment. The Research has been actively processed in e-commerce so as to improve the accuracy of recommendation. Existing Collaborative filtering (CF) can not reflect contents of the items and has the problem of the process of selection in the neighborhood user group and the problems of sparsity and scalability as well. Although a system has been practically used to improve these defects, it still does not reflect attributes of the item. In this paper, to solve this problem, We can use a implicit method which is used by customer's data and purchase history data. We propose a new clustering method of weighted preference for customer using k-means clustering and Bayesian network in order to improve the accuracy of recommendation. To verify improved performance of the proposed system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1872-1879
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• 2016

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.

• 한국경영정보학회:학술대회논문집
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• 한국경영정보학회 2007년도 International Conference
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• pp.318-323
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• 2007

국방부에서 발표한 ‘국방개혁에 관한 법률’ 에 따라 2014년까지 현역병들에 대한 복무기간이 단계적으로 단축될 예정이다. 이에 따라 좀 더 효율적인 직무교육 방안이 필요하게 되어, ‘차등제 교육’을 시행하고 있다. 이 교육의 효과를 향상시키기 위해서는 훈련병들의 예상 학업 성취도를 미리 정확하게 예측하는 것이 필수적이다. 따라서, 본 연구에서는 입교 초기에 얻을 수 있는 신병들의 제한된 자료들을 이용하여 교육 성취도 예측 모형을 개발하였다. 본 모형의 목적 변수는 ‘일반관리 인원’, ‘집중관리 인원’의 값을 갖는 이진형 성취집단 변수이며, 사용된 기법은 k-means 군집기볍과 Decision Tree 기법을 혼합한 모형, k-means 군집기법과 Neural Network 기법을 혼합한 모형, Decision Tree 모형, Neural Network 모형, Bayesian 모형, 그리고 Logistic 모형 등을 사용하였다. 그 결과 k-means 군집기법과 Decision Tree를 혼합한 모형이 가장 좋은 예측력올 보이는 것으로 나타났다. 이러한 교육 성취집단 예측 모형은 향후 군에서 이루어지는 다양한 교육 프로그램에 적극적으로 이용될 수 있을 것으로 기대된다.

• 한국음향학회지
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• 제21권1호
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• pp.69-72
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• 2002

부호책 설계에 사용되는 초기 부호책을 얻기 위해 수정된 미소분리 방법을 제안한다. 제안된 방법은 다른 클래스에 비해 소속되는 학습벡터가 다수 소속되거나 자승오차가 작은 클래스에 대표벡터를 더 많이 할당한다는 원리를 적용한다. 기존 K-means 알고리즘과 참고문헌 (5)에서 제안한 방법을 적용하여 설계된 부호책의 성능을 평가할 때, 두 경우 모두 제안된 방법에 의해 얻어진 초기 부호책을 사용하는 것이 기존 미소분리 방법에 의한 초기 부호책을 사용하는 것보다 우수한 결과를 나타낸다.

• 대한음성학회지:말소리
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• 제57호
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• pp.113-122
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• 2006

Recently, many studies have been done for speech recognition in noisy environments. Particularly, the Aurora DB has been built as the common database for comparing the various feature extraction schemes. However, in general, the recognition models as well as the features have to be modified for effective noisy speech recognition. As the structure of the HTK is very complex, it is not easy to modify, the recognition engine. In this paper, we implemented a baseline recognizer based on the segmental K-means algorithm whose performance is comparable to the HTK in spite of the simplicity in its implementation.

• 대한한의학원전학회지
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• 제25권3호
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• pp.51-56
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• 2012

Objective : Every language has twofold aspects in meaning of characters such as 'lead' means guide as a verb and 'lead' means one of metals. Chinese character has several meanings because it is phonogram, even if those are exactly same in shapes. we have concern how different meaning of the character 'Si(死)' is in traditional medical classics. Method : we try to find out the meaning of 'Si' excluding meaning of 'Si', 'death' in "Huangdineijing". Result : 'Si' means 'death' primary and means 'serious disease to death', 'losing consciousness just like death' and so on. Conclusion : Someone who have concern traditional medical classics have to give attentions that characters in classics could get different meanings in same character.

• Asia pacific journal of information systems
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• 제16권1호
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• pp.127-144
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• 2006

The K-means algorithm is widely used at the initial stage of data analysis in data mining process, partly because of its low time complexity and the simplicity of practical implementation. Cluster validity indices are used along with the algorithm in order to determine the number of clusters as well as the clustering results of datasets. In this paper, we present a performance comparison of sixteen indices, which are selected from forty indices in literature, while considering their applicability to nonhierarchical clustering algorithms. Data sets used in the experiment are generated based on multivariate normal distribution. In particular, four error types including standardization, outlier generation, error perturbation, and noise dimension addition are considered in the comparison. Through the experiment the effects of varying number of points, attributes, and clusters on the performance are analyzed. The result of the simulation experiment shows that Calinski and Harabasz index performs the best through the all datasets and that Davis and Bouldin index becomes a strong competitor as the number of points increases in dataset.

• 융합신호처리학회논문지
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• 제16권1호
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• pp.9-14
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• 2015

Outlier detection and removal is a crucial step needed for various image processing applications such as image registration. Random Sample Consensus (RANSAC) is known to be the best algorithm so far for the outlier detection and removal. However RANSAC requires a cosiderable computation time. To drastically reduce the computation time while preserving the comparable quality, a outlier detection and removal method based on modified K-means is proposed. The original K-means was conducted first for matching point pairs and then cluster merging and member exclusion step are performed in the modification step. We applied the methods to various images with highly repetitive patterns under several geometric distortions and obtained successful results. We compared the proposed method with RANSAC and showed that the proposed method runs 3~10 times faster than RANSAC.

• 한국전기전자재료학회논문지
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• 제20권11호
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• pp.959-964
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• 2007

To investigate the influence of partial discharge(PD) distribution characteristics due to various defects on the power cable joints interface, we used the K-means clustering method. As the result of PD number(n) distribution analyzing on $\Phi-n$ graph, the phase angle($\Phi$) of cluster centroid shifted to $0^{\circ}\;and\;180^{\circ}$ increasing with applying voltage. It was confirmed that the PD quantify(q) and euclidean distance of centroid were increased with applying voltage from the centroid distribution analyzing of $\Phi-q$ plane. The dispersion degree was increased with calculated standard deviation of the $\Phi-q$ cluster centroid. The PD number and mean value on $\Phi-q$ graph were some different by electric field concentration with defect types.