• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.267-275
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• 2004

Directed graph and Lindenmayer system (L-system) are two major encoding methods of representation to develop creatures in application field of artificial life. It is difficult to define real morphology structurally using the L-systems which are a grammatical rewriting system because L-systems represent genotype as loops, procedure calls, variables, and parameters. This paper defines a class of representations called structured directed graph, which is identified by its ability to define structures of the genotype in the translation to the phenotype, and presents an example of creating 3D flowers using a directed graph which is proper method to represent real morphology, and interactive genetic algorithm which decodes the problem with human's emotional evaluation. The experimental results show that natural flower morphology can be generated by the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.702-705
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• 2003

Directed graph and Lindenmayer system (L-system) are two major encoding methods of representation to develop creatures in an application field of artificial life. It is difficult to structurally define real morphology using the L-systems which are a grammatical rewriting system because they represent genotype as loops, procedure calls, variables, and parameters. This paper defines a class of representations called structured directed graph and interactive genetic algorithm for automatically creating 3D flower morphology. The experimental results show that natural flower morphology can be created by the proposed method.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.267-271
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• 2005

The goal of data mining is to extract new and useful knowledge from large scale datasets. As the amount of available data grows explosively, it became vitally important to develop faster data mining algorithms for various types of data. Recently, an interest in developing data mining algorithms that operate on graphs has been increased. Especially, mining frequent patterns from structured data such as graphs has been concerned by many research groups. A graph is a highly adaptable representation scheme that used in many domains including chemistry, bioinformatics and physics. For example, the chemical structure of a given substance can be modelled by an undirected labelled graph in which each node corresponds to an atom and each edge corresponds to a chemical bond between atoms. Internet can also be modelled as a directed graph in which each node corresponds to an web site and each edge corresponds to a hypertext link between web sites. Notably in bioinformatics area, various kinds of newly discovered data such as gene regulation networks or protein interaction networks could be modelled as graphs. There have been a number of attempts to find useful knowledge from these graph structured data. One of the most powerful analysis tool for graph structured data is frequent subgraph analysis. Recurring patterns in graph data can provide incomparable insights into that graph data. However, to find recurring subgraphs is extremely expensive in computational side. At the core of the problem, there are two computationally challenging problems. 1) Subgraph isomorphism and 2) Enumeration of subgraphs. Problems related to the former are subgraph isomorphism problem (Is graph A contains graph B?) and graph isomorphism problem(Are two graphs A and B the same or not?). Even these simplified versions of the subgraph mining problem are known to be NP-complete or Polymorphism-complete and no polynomial time algorithm has been existed so far. The later is also a difficult problem. We should generate all of 2$^n$ subgraphs if there is no constraint where n is the number of vertices of the input graph. In order to find frequent subgraphs from larger graph database, it is essential to give appropriate constraint to the subgraphs to find. Most of the current approaches are focus on the frequencies of a subgraph: the higher the frequency of a graph is, the more attentions should be given to that graph. Recently, several algorithms which use level by level approaches to find frequent subgraphs have been developed. Some of the recently emerging applications suggest that other constraints such as connectivity also could be useful in mining subgraphs : more strongly connected parts of a graph are more informative. If we restrict the set of subgraphs to mine to more strongly connected parts, its computational complexity could be decreased significantly. In this paper, we present an efficient algorithm to mine frequent subgraphs that are more strongly connected. Experimental study shows that the algorithm is scaling to larger graphs which have more than ten thousand vertices.

• Journal of Korea Spatial Information System Society
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• v.7 no.1 s.13
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• pp.39-52
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• 2005

GML (Geography Markup Language) has become the widely adopted standard for transport and storage of geographic information. So, various researches such as modeling, storage, query, and etc have been studied to provide the interoperability of geographic information in web environments. Especially, there are increased needs to store semi-structured data such as GML documents efficiently. Therefore, in this paper, we design and implement a GML repository to store GML documents on the basis of GML schema using spatial database system. GML Schema is converted into directed GML schema graph and the schema mapping technique from directed schema graph to spatial schema is presented. Also, we define the conversion rules on spatial schema to preserve the constraints of GML schema. GML repository using spatial database system is useful to provide the interoperability of geographic information and to store and manage enormous GML documents.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.4
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• pp.63-82
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• 2008

The explosively growing attractiveness of the Web is commencing significant demands for a structuring analysis on various web objects. The larger the substantial number of web objects are available, the more difficult for the clients(i.e. common web users and web robots) and the servers(i.e. Web search engine) to retrieve what they really want. We have in mind focusing on the structure of web objects by introducing optimization models for more convenient and effective information retrieval. For this purpose, we represent web objects and hyperlinks as a directed graph from which the optimal structures are derived in terms of rooted directed spanning trees and Top-k trees. Computational experiments are executed for synthetic data as well as for real web sites' domains so that the Lagrangian Relaxation approaches have exploited the Top-k trees and Hop constraint resolutions. In the experiments, our methods outperformed the conventional approaches so that the complex web graph can successfully be converted into optimal-structured ones within a reasonable amount of computation time.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.3
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• pp.43-49
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• 2012

Information obtained from DFD(Data Flow Diagram) are very important in system maintenance, because most legacy systems are analyzed using DFD in structured analysis. In our thesis, we design and implement an extractor for design sequence of database tables using DFD. Our extractor gets DFDs as input data, transform them into a directed graph, and extract design sequence of DB tables. We show practicality of our extractor by applying it to a s/w system in operation.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.309-320
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• 2020

Bayesian networks, also known as directed acyclic graphs (DAG), are used in many areas of medicine, meteorology, and genetics because relationships between variables can be modeled with graphs and probabilities. In particular, Bayesian network classifiers, which are used to predict discrete data, have recently become a new method of data mining. Bayesian networks can be grouped into different models that depend on structured learning methods. In this study, Bayesian network models are learned with various properties of structure learning. The models are compared to the simplest method, the naïve Bayes model. Classification results are compared by applying learned models to various real data. This study also compares the relationships between variables in the data through graphs that appear in each model.

• Genomics & Informatics
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• v.7 no.2
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• pp.122-130
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• 2009

A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selected molecular functions include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting perturbed molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.