• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.729-733
• /
• 2003

The presence of local features within clusters incurred by multi-modal nature of data prohibits many conventional clustering techniques from working properly. Especially, the clustering of datasets with non-Gaussian distributions within a cluster can be problematic when the technique with implicit assumption of Gaussian distribution is used. Current study proposes a simple tandem clustering method composed of k-means type algorithm and hierarchical method to solve such problems. The multi-modal dataset is first divided into many small pre-clusters by k-means or fuzzy k-means algorithm. The pre-clusters found from the first step are to be clustered again using agglomerative hierarchical clustering method with Kullback- Leibler divergence as the measure of dissimilarity. This method is not only effective at extracting the multi-modal clusters but also fast and easy in terms of computation complexity and relatively robust at the presence of outliers. The performance of the proposed method was evaluated on three generated datasets and six sets of publicly known real world data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.341-343
• /
• 2019

As the generation of high concentration particulate matter increases, much attention is focused on the prediction of particulate matter. Particulate matter refers to particulate matter less than $10{\mu}m$ diameter in the atmosphere and is affected by weather changes such as temperature, relative humidity and wind speed. Therefore, various studies have been conducted to analyze the correlation with weather information for particulate matter prediction. However, the nonlinear time series distribution of particulate matter increases the complexity of the prediction model and can lead to inaccurate predictions. In this paper, we try to mitigate the nonlinear characteristics of particulate matter by using cluster algorithm and classification algorithm of machine learning. The machine learning algorithms used are agglomerative clustering, density-based spatial clustering of applications with noise(DBSCAN).

• Communications for Statistical Applications and Methods
• /
• v.13 no.3
• /
• pp.719-732
• /
• 2006

A set of clustering algorithms with proper weight on the formulation of distance which extend to mixed numeric and multiple binary values is presented. A simple matching and Jaccard coefficients are used to measure similarity between objects for multiple binary attributes. Similarities are converted to dissimilarities between i th and j th objects. The performance of clustering algorithms with balancing weight on different similarity measures is demonstrated. Our experiments show that clustering algorithms with application of proper weight give competitive recovery level when a set of data with mixed numeric and multiple binary attributes is clustered.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.941-949
• /
• 2011

In this paper, 72 weather stations in Korea are clustered by the hierarchical agglomerative procedure based on the average linkage method. We compare our clusters and stations divided by mountain chains which are applied to study on the impact analysis of foodborne disease outbreak due to climate change.

• Journal of the Korean Society for information Management
• /
• v.23 no.4 s.62
• /
• pp.215-231
• /
• 2006

Recently there are many bibliometric studies attempting to utilize Pathfinder networks(PFNets) for examining and analyzing the intellectual structure of a scholarly field. Pathfinder network scaling has many advantages over traditional multidimensional scaling, including its ability to represent local details as well as global intellectual structure. However there are some limitations in PFNets including very high time complexity. And Pathfinder network scaling cannot be combined with cluster analysis, which has been combined well with traditional multidimensional scaling method. In this paper, a new method named as Parallel Nearest Neighbor Clustering (PNNC) are proposed for complementing those weak points of PFNets. Comparing the clustering performance with traditional hierarchical agglomerative clustering methods shows that PNNC is not only a complement to PFNets but also a fast and powerful clustering method for organizing informations.

• The Plant Pathology Journal
• /
• v.34 no.6
• /
• pp.544-554
• /
• 2018

Tomato (Solanum lycopersicum) production in Ghana is limited by the root-knot nematode (Meloidogyne incognita, and yield losses over 70% have been experienced in farmer fields. Major management strategies of the root-knot nematode (RKN), such as rotation and nematicide application, and crop rotation are either little efficient and harmful to environments, with high control cost, respectively. Therefore, this study aims to examine morphometric variations of RKN populations in Ghana, using principal component analysis (PCA), of which the information can be utilized for the development of tomato cultivars resistant to RKN. Ninety (90) second-stage juveniles (J2) and 16 adult males of M. incognita were morphometrically characterized. Six and five morphometric variables were measured for adult males and second-stage juveniles (J2) respectively. Morphological measurements showed differences among the adult males and second-stage juveniles (J2). A plot of PC1 and PC2 for M. incognita male populations showed clustering into three main groups. Populations from Asuosu and Afrancho (Group I) were more closely related compared to populations from Tuobodom and Vea (Group II). There was however a single nematode from Afrancho (AF4) that fell into Group III. Biplots for male populations indicate, body length, DEGO, greatest body width, and gubernaculum length serving as variables distinguishing Group 1 and Group 2 populations. These same groupings from the PCA were reflected in the dendogram generated using Agglomerative Hierarchical Clustering (AHC). This study provides the first report on morphometric characterisation of M. incognita male and juvenile populations in Ghana showing significant morphological variation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.8
• /
• pp.744-750
• /
• 2010

To segment instances of vehicle classes in a sparse stereo-matching data set, this paper presents an algorithm for clustering based on DP (Dynamic Programming). The algorithm is agglomerative: it begins with each element in the set as a separate cluster and merges them into successively larger clusters according to similarity of two clusters. Here, similarity is formulated as a cost function of DP. The proposed algorithm is proven to be effective by experiments performed on various images acquired by a moving vehicle.

• Korean Journal of Remote Sensing
• /
• v.20 no.3
• /
• pp.215-225
• /
• 2004

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using the conventional agglomerative approach. Using simulation data, the proposed method was compared with another hierarchical clustering technique based on 'mutual closest neighbor.' The experimental results show that the new approach proposed in this study considerably increases in computational efficiency for larger images with a low number of bands. The technique was then applied to classify the land-cover types using the remotely-sensed data acquired from the Korean peninsula.

• KIISE Transactions on Computing Practices
• /
• v.21 no.6
• /
• pp.436-441
• /
• 2015

There are many types of clustering algorithms such as centroid, hierarchical, or density-based methods. Each algorithm has unique data grouping principles, which creates different varieties of clusters. Ordering Points To Identify the Clustering Structure (OPTICS) is a well-known density-based algorithm to analyze arbitrary shaped and varying density clusters, but the obtained clusters only correlate loosely. Hierarchical agglomerative clustering (HAC) reveals a hierarchical structure of clusters, but is unable to clearly find non-convex shaped clusters. In this paper, we provide a novel hierarchy generation framework and application which can aid users by combining the advantages of the two clustering methods.

• The Korean Journal of Applied Statistics
• /
• v.18 no.3
• /
• pp.555-565
• /
• 2005

To Know customers is very important for the company to survive in its cut-throat competition among coimpetitors. Companies need to manage the relationship with each ana every customer, ant make each of customers as profitable as possible. CRM (Customer relationship management) has emerged as a key solution for managing the profitable relationship. In order to achieve successful CRM customer segmentation is a essential component. Clustering as a data mining technique is very useful to build data-driven segmentation. This paper is concerned with building proper customer segmentation with introducing a credit card company case. Customer segmentation was built based only on transaction data which cattle from customer's activities. Two-step clustering approach which consists of k-means clustering and agglomerative clustering was applied for building a customer segmentation.