• Journal of KIISE:Software and Applications
• /
• v.33 no.8
• /
• pp.730-740
• /
• 2006

In recent years, bullied students and rogue groups in teenagers make many serious social problems. In this paper we propose a novel approach that more objectively analyzes peer relationships among students. As the data for objective analysis, we use communication history records that are collected from various communication tools such as telephones, e-mails, and messengers. We use the simple intuition that communication history records implicitly contain peer relationship information. And, we adopt data mining techniques for the more systematic analysis. The proposed peer relationship analysis consists of the following steps. First, we formally define the notion of degree of familiarity between friends, and present mathematical equations that compute the degree based on communication history records. In the proposed method, we use the intuition that the degree of familiarity from student x to student y becomes higher as x makes the more communications with y. Second, by using the degree of familiarity between students, we find out the students who are potentially bullied. This procedure is based on the assumption that a bullied student may have a very small number of history records from other students to him. Third, we adopt the clustering technique, one of the most representative data mining techniques, to find out meaningful student groups by using the degree of familiarity. To use the clustering technique, we formally define the notion of similarity between friends based on the degree of familiarity, and perform clustering using the notion. Last, to show the practicality of the proposed method, we have implemented the method and interpreted the meaning of the experimental results. Overall, we believe that our research result provides an effective framework that analyzes peer relationships more objectively and more systematically.

• Journal of Digital Convergence
• /
• v.12 no.12
• /
• pp.293-301
• /
• 2014

In this paper, we propose a new measure based on the confidence of Bayesian posterior probability so as to reduce unimportant information in the clustering process. Because the performance of clustering is up to selecting the important degree of attributes within the databases, the concept of information entropy is added to posterior probability for attributes discernibility. Hence, The same value of attributes in the confidence of the proposed measure is considerably much less due to the natural logarithm. Therefore posterior probability-based clustering algorithm selects the minimum of attribute reducts and improves the efficiency of clustering. Analysis of the validation of the proposed algorithms compared with others shows their discernibility as well as ability of clustering to handle uncertainty with ACME categorical data.

• Journal of Information Processing Systems
• /
• v.12 no.4
• /
• pp.555-576
• /
• 2016

In the conventional clustering algorithms, an object could be assigned to only one group. However, this is sometimes not the case in reality, there are cases where the data do not belong to one group. As against, the fuzzy clustering takes into consideration the degree of fuzzy membership of each pixel relative to different classes. In order to overcome some shortcoming with traditional clustering methods, such as slow convergence and their sensitivity to initialization values, we have used the Harmony Search algorithm. It is based on the population metaheuristic algorithm, imitating the musical improvisation process. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. We propose in this paper a new unsupervised clustering method called the Fuzzy Harmony Search-Fourier Transform (FHS-FT). It is based on hybridization fuzzy clustering and the harmony search algorithm to increase its exploitation process and to further improve the generated solution, while the Fourier transform to increase the size of the image's data. The results show that the proposed method is able to provide viable solutions as compared to previous work.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.9
• /
• pp.2353-2360
• /
• 1998

In the past. as the environment of the established soft ware system changed, most Re-Engineering perforned clustering on the basis of logical operation, In contrast, this paper proposes a method to perfonn clustering efficiently using the infonmltion sharing of each modult, of source programs that constitute the software For the clustering of related modules using the information sharing. We evaluated the result after measuring the degree of clustering using similarity and uniqueness algorithm on the basis of heuristic method of measurement. Thus, we could manipulate and achieve the clustering of related modules and procedures, This paper also prests a method to reconstruct the software system efficiently through the clustering and shows the possibility of its realization through real example.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.27 no.2
• /
• pp.146-159
• /
• 2023

This paper proposes a hybrid algorithm combining K-means clustering and watershed algorithms for flower segmentation and counting. We use the K-means clustering algorithm to obtain the main colors in a complex background according to the cluster centers and then take a color space transformation to extract pixel values for the hue, saturation, and value of flower color. Next, we apply the threshold segmentation technique to segment flowers precisely and obtain the binary image of flowers. Based on this, we take the Euclidean distance transformation to obtain the distance map and apply it to find the local maxima of the connected components. Afterward, the proposed algorithm adaptively determines a minimum distance between each peak and apply it to label connected components using the watershed segmentation with eight-connectivity. On a dataset of 30 images, the test results reveal that the proposed method is more efficient and precise for the counting of overlapped flowers ignoring the degree of overlap, number of overlap, and relatively irregular shape.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.629-630
• /
• 2008

MANET is depended on the property as like variable energy, high degree of mobility, location environments of nodes etc. So, in this paper, we propose an algorithm techniques which is TICC (Time Interval Clustering Control) based on energy value in property of each node for solving cluster problem. It provides improving cluster energy efficiency how can being node manage to order each node's energy level. TICC is clustering method. It has shown that Node's energy efficiency and life time are improved in MANET.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.2
• /
• pp.262-267
• /
• 2008

The focus of this paper is on devising an efficient clustering task for arbitrary color data. In order to tackle this problem, the inherent uncertainty and vagueness of color are represented by a fuzzy color model. By taking a fuzzy approach to color representation, the proposed model makes a soft decision for the vague regions between neighboring colors. A definition on a three-dimensional fuzzy color ball is introduced, and the degree of membership of color is computed by employing a distance measure between a fuzzy color and color data. With the fuzzy color model, a novel fuzzy clustering algorithm for efficient partition of color data is developed.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.3
• /
• pp.97-107
• /
• 1997

Image segmentation is teh important step in image infromation extraction for computer vison sytems. Fuzzy clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are derived from the fuzzy c-means (FCM) algorithm. The FCM algorithm uses th eprobabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belongingor compatibility. moreover, the FCM algorithm has considerable trouble above under noisy environments in the feature space. Recently, the possibilistic C-mean (PCM) for solving growing for color image segmentation. In the PCM, the membersip values may be interpreted as degrees of possibility of the data points belonging to the classes. So, the problems in the FCM can be solved by the PCM. The clustering results by just PCM are not smoothly bounded, and they often have holes. So, the region growing was used as a postprocessing. In our experiments, we illustrated that the proposed method is reasonable than the FCM in noisy enviironments.

• Industrial Engineering and Management Systems
• /
• v.14 no.3
• /
• pp.236-247
• /
• 2015

This study aims to examine the effects of industrial clustering and manufacturing flexibility on innovation capability and operational performance. This study follow a survey method to collect data pertaining to the phenomena of industrial clustering, manufacturing flexibility, innovation capability, and operational performance by utilizing a single respondent design. A total of 124 Indonesian manufacturing SMEs are taken to test the proposed theoretical model by utilizing covariance-based structural equations modeling approach. It was found that both industrial clustering and manufacturing flexibility was positively associated with operational performance and innovation capability as well. In addition, innovation capability may account for the effects of industrial clustering and manufacturing flexibility on operational performance. This implies that manufacturing SMEs have to reorient their production and operation perspectives, including agglomerate with other similar or related SMEs to develop and utilize their own resources. The SMEs also need to possess some degree of manufacturing flexibility in respond to the uncertain environment and market changes. In addition, the SMEs should put a greater emphasize to use industrial cluster and manufacturing flexibility benefits to generate innovation capability to achieve high performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 1999.05a
• /
• pp.109-112
• /
• 1999

Image segmentation is the critical first step in image information extraction for computer vision systems. Clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are divided from the fuzzy c-means(FCM) algorithm. The FCM algorithm uses fie probabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belonging or compatibility. Moreover, the FCM algorithm has considerable trouble under noisy environments in the feature space. Recently, a possibilistic approach to clustering(PCM) for solving above problems was proposed. In this paper, we used the PCM for color image segmentation. This approach differs from existing fuzzy clustering methods for color image segmentation in that the resulting partition of the data can be interpreted as a possibilistic partition. So, the problems in the FCM can be solved by the PCM. But, the clustering results by the PCM are not smoothly bounded, and they often have holes. The region growing was used as a postprocessing after smoothing the noise points in the pixel seeds. In our experiments, we illustrate that the PCM us reasonable than the FCM in noisy environments.