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

• International Journal of Internet, Broadcasting and Communication
• /
• 제6권1호
• /
• pp.16-22
• /
• 2014

Clustering methods such as k-means and EM are the group of classification and pattern recognition, which are used in management science and literature search widely. In this paper, k-means and EM algorithm are compared the performance using by Weka. The winning Lottery numbers of 567 cases are experimented for our study and presentation. Processing speed of the k-means algorithm is superior to the EM algorithm, which is about 0.08 seconds faster than the other. As the result it is summerized that EM algorithm is better than K-means algorithm with comparison of accuracy, precision and recall. While K-means is known to be sensitive to the distribution of data, EM algorithm is probability sensitive for clustering.

• Journal of the Korean Data and Information Science Society
• /
• 제16권3호
• /
• pp.557-566
• /
• 2005

Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper we used k-means clustering of several clustering techniques. The k-means Clustering Is classified as a partitional clustering method. We analyze 2002 Gyeongnam social indicator survey data using k-means clustering techniques for environmental information. We can use these outputs given by k-means clustering for environmental preservation and environmental improvement.

• 한국데이터정보과학회:학술대회논문집
• /
• 한국데이터정보과학회 2004년도 추계학술대회
• /
• pp.77-86
• /
• 2004

Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper we used k-means clustering of several clustering techniques. The k-means Clustering is classified as a partitional clustering method. We analyze 2002 Gyeongnam social indicator survey data using k-means clustering techniques for environmental information. We can use these outputs given by k-means clustering for environmental preservation and environmental improvement.

• 대한간호학회지
• /
• 제35권7호
• /
• pp.1362-1370
• /
• 2005

Purpose: The purpose of this study was to identify the degree of attaining a smoking cessation goal when an agreement on means to achieve smoking cessation among male college student smokers was established. Method: This study was planned as a nonequivalent control group non-synchronized design and the sample was divided into an agreement group and a comparison group by convenience sampling in a college of G city. The data was analysed with SPSS Win10.0 using a Likelihood $x^2-test$, Odds ratio, Paired t-test and ANCOVA. Result: The theory that the degree of smoking cessation will be higher in the agreement group than the Comparison group was rejected (${\delta}$ = 2.567, p = .055). The theory that nicotine dependency will be lower in the agreement group than the comparison group was supported (F = 3.965, P = .049); however, the theory that the number of cigarettes smoked per day will be lower in the agreement group than the comparison group was rejected (F = 1.342, p = .252). Conclusion: It has been shown that an agreement on means to achieve smoking cessation goals is a key factor to success in quitting smoking.

• 대한인간공학회지
• /
• 제12권2호
• /
• pp.85-91
• /
• 1993

Data obtained for sensory evaluation have a wide dispersion and fuzziness since human sensory organs are used as a means of measuring sensation instead of measuring instruments. Such dispersion and fuzziness are caused by all kinds of time error and have a great influence on the sensory evaluation, but most of previous papers not consider time errors. In this study, the comparative judgement capability of the evaluator was discriminated by means of the eigen- structure analysis on the premise that evaluation values of sensory evaluators obtained by a paired-comparison become different by the order of sample presentation.

• 대한인간공학회:학술대회논문집
• /
• 대한인간공학회 1993년도 추계학술대회논문집
• /
• pp.113-123
• /
• 1993

In a sensory evaluation, the data obtained by a result of evaluation have a wide dispersion and fuzziness because human sense organ is used as a means of measuring sensation instead of measuring instruments. These dispersion and fuzziness are caused by all kinds of time error and have a great influence on a sensory evaluation, but most of previous papers don't deal with these time errors. In this study, a comparative judgement capacity of an evaluator is discriminated by means of the eigen-structure analysis on the premise that evaluation value of sensory evaluators obtained by a paired-comparison become different by the order of sample presentation

• Journal of Information Processing Systems
• /
• 제8권3호
• /
• pp.389-398
• /
• 2012

Texture is an important visual feature for image analysis. Many approaches have been proposed to model and analyze texture features. Although these approaches significantly contribute to various image-based applications, most of these methods are sensitive to the changes in the scale and orientation of the texture pattern. Because textures vary in scale and orientations frequently, this easily leads to pattern mismatching if the features are compared to each other without considering the scale and/or orientation of textures. This paper suggests an Orientation Matching Scheme (OMS) to ease the problem of mismatching rotated patterns. In OMS, a pair of texture features will be compared to each other at various orientations to identify the best matched direction for comparison. A database including rotated texture images was generated for experiments. A synthetic retrieving experiment was conducted on the generated database to examine the performance of the proposed scheme. We also applied OMS to the similarity computation in a K-means clustering algorithm. The purpose of using K-means is to examine the scheme exhaustively in unpromising conditions, where initialized seeds are randomly selected and algorithms work heuristically. Results from both types of experiments show that the proposed OMS can help improve the performance when dealing with rotated patterns.

• 응용통계연구
• /
• 제19권3호
• /
• pp.447-460
• /
• 2006

본 연구에서는 확률모형의 모수로부터 얻어지는 여러 형태의 함수간의 크기를 다중 비교 하는 방법을 제안하고자 한다. 이 방법은 비교대상인 모수 함수 간의 선호확률을 베이지안 방법으로 추정하고, 이들로부터 얻어지는 선호행렬을 이용한 새로운 다중비교법이다. 이러한 방법의 제안에 필요한 이론과 비교기준을 고안하였으며, 응용 예로 제안된 방법을 s의 독립인 지수분포 모수의 기하평균 크기 비교에 적용하였다.

• 충청수학회지
• /
• 제29권1호
• /
• pp.123-135
• /
• 2016

In this article, we introduce the transforms of Pythagorean and quadratic means of weighted shifts. We then explore how the transforms of weighted shifts behaves, in comparison with the Aluthge transform.