• Journal of Multimedia Information System
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• v.6 no.1
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• pp.37-42
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• 2019

Skyline queries are used in a variety of fields to make optimal decisions. However, as the volume of data and the dimension of the data increase, the number of skyline points increases with the amount of time it takes to discover them. Mainly, because the number of skylines is essential in many real-life applications, various studies have been proposed. However, previous researches have used the k-parameter methods such as top-k and k-means to discover representative skyline points (RSPs) from entire skyline point set, resulting in high query response time and reduced representativeness due to k dependency. To solve this problem, we propose a new Connected Component Clustering based Representative Skyline Query (3CRS) that can discover RSP quickly even in high-dimensional data through connected component clustering. 3CRS performs fast discovery and clustering of skylines through hash indexes and connected components and selects RSPs from each cluster. This paper proves the superiority of the proposed method by comparing it with representative skyline queries using k-means and DBSCAN with the real-world dataset.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.494-499
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• 2015

As the size of data increases, it becomes important to identify properties by analyzing big data. In this paper, we propose a k-Means based efficient clustering technique, called MCSKMeans (Multi centroid set k-Means), using distributed parallel processing framework MapReduce. A problem with the k-Means algorithm is that the accuracy of clustering depends on initial centroids created randomly. To alleviate this problem, the MCSK-Means algorithm reduces the dependency of initial centroids using sets consisting of k centroids. In addition, we apply the agglomerative hierarchical clustering technique for creating k centroids from centroids in m centroid sets which are the results of the clustering phase. In this paper, we implemented our MCSK-Means based on the MapReduce framework for processing big data efficiently.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.1044-1049
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• 2006

Multiple sequence alignment is a method to compare two or more DNA or protein sequences. Most of multiple sequence alignment tools rely on pairwise alignment and Smith-Waterman algorithm to generate an alignment hierarchy. Therefore, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST and CDD (Conserved Domain Database)search were combined with a clustering tool. Our clustering and annotating tool consists of constructing suffix tree, overlapping common subsequences, clustering gene sequences and annotating gene clusters by BLAST and CDD search. The system was successfully evaluated with 36 gene sequences in the pentose phosphate pathway, clustering 10 clusters, finding out representative common subsequences, and finally identifying functional domains by searching CDD database.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3672-3679
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• 2012

Vehicle Ad hoc Network (VANET) is next-generation network technology to provide various services using V2V (Vehicle-to-Vehicle) and V2I (Vehicle-to-Infrastructure). In VANET, many researchers proposed various studies for the safety of drivers. In particular, using the emergency message to increase the efficiency of traffic safety have been actively studied. In order to efficiently transmit to moving vehicle, to send a quick message to as many nodes is very important via broadcasting belong to communication range of vehicle nodes. However, existing studies have suggested a message for transmission to the communication node through indiscriminate broadcasting and broadcast storm problems, thereby decreasing the overall performance has caused the problem. In addition, theses problems has decreasing performance of overall network in various form of road and high density of vehicle node as urban area. Therefore, this paper proposed Clustering based Routing Algorithm (CBRA) to efficiently transmit emergency message in high density of vehicle as urban area. The CBRA managed moving vehicle via clustering when vehicle transmit emergency messages. In addition, we resolve linkage problem between vehicles according to various form of road. The CBRA resolve link brokage problem according to various form of road as urban using clustering. In addition, we resolve broadcasting storm problem and improving efficacy using selection flooding method. simulation results using ns-2 revealed that the proposed CBRA performs much better than the existing routing protocols.

• Proceedings of the CALSEC Conference
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• 2004.02a
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• pp.294-301
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• 2004

·Text-mining approach to e-documents organization based on topic hierarchy - Machine-Learning ＆ information Theory-based ㆍ 'Category(topic) discovery' problem → document bundle-based user-constraint document clustering ㆍ 'Automatic categorization' problem → Accelerated EM with CU-based active learning → 'Hierarchy Construction' problem → Unsupervised learning of category subsumption relation

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.197-201
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• 2005

When we want to find out the regulatory relationships between genes from gene expression data, dimensionality is one of the big problem. In general, the size of search space in modeling the regulatory relationships grows in O(n$^2$) while the number of genes is increasing. However, hopefully it can be reduced to O(kn) with selected k by applying divide and conquer heuristics which depend on some assumptions about genetic network. In this paper, we approach the modeling problem in divide-and-conquer manner. We applied clustering to make the problem into small sub-problems, then hierarchical model process is applied to those small sub-problems.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.3
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• pp.281-288
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• 2001

We consider in this article a multi-objective part-machine grouping problem in which parts have alternative process plans and expected annual demand of each part is known. This problem is characterized as optimally determining part sets and corresponding machine cells such that total sum of distance (or dissimilarity) between parts and total sum of load differences between machines are simultaneously minimized. Two heuristic algorithms are proposed, and examples are given to compare the performance of the algorithms.

• Journal of Mechanical Science and Technology
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• v.18 no.12
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• pp.2137-2147
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• 2004

The recycling cell formation problem means that disposal products are classified into recycling part families using group technology in their end-of-life phase. Disposal products have the uncertainties of product status by usage influences during product use phase, and recycling cells are formed design, process and usage attributes. In order to deal with the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem for disposal products. Fuzzy C-mean algorithm and a heuristic approach based on fuzzy ART neural network is suggested. Especially, the modified Fuzzy ART neural network is shown that it has a good clustering results and gives an extension for systematically generating alternative solutions in the recycling cell formation problem. Disposal refrigerators are shown as examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2017-2036
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• 2012

Based on the current solutions to the problem of energy hole, this paper proposed a nonuniform node distribution clustering algorithm, NNDC. Firstly, we divide the network into rings, and then have an analysis and calculation on nodes' energy consumption in each ring of the network when clustering algorithm is applied to collect data. We also put forward a scheme of nonuniform node distribution on the basis of the proportion of nodes' energy consumption in each ring, and change nodes' active/hibernating states under density control mechanism when network coverage is guaranteed. Simulation shows NNDC algorithm can satisfyingly balance nodes' energy consumption and effectively avoid the problem of energy hole.

• Korean Management Science Review
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• v.24 no.2
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• pp.43-56
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• 2007

VRP(Vehicle Routing Problem) is studied in this paper, where two different kinds of missions are to be completed. The objective is to minimize the total vehicle operating distance. A mixed integer programming formulation and a heuristic algorithm for a practical use are suggested. A heuristic algorithm consists of three phases such as clustering, constructing routes, and adjustment. In the first phase, customers are clustered so that the supply nodes are grouped with demand nodes to be served by the same vehicle. Vehicle routes are generated within the cluster in the second phase. Clusters and routes are adjusted in the third phase using the UF (unfitness) rule designed to determine the customers and the routes to be moved properly. It is shown that the suggested heuristic algorithm yields good performances within a relatively short computational time through computational experiment.