• Title/Summary/Keyword: KDD(Knowledge Discovery in Database)

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Implementation of Management performance Analysis System with KDD (KDD에 기반한 경영성과 분석 시스템 구현)

  • An, Dong-Gyu;Jo, Seong-Hun
    • 한국디지털정책학회:학술대회논문집
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    • 2004.05a
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    • pp.575-592
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    • 2004
  • In modern dynamic management environment, there is growing recognition that? information & knowledge management systems are essential for CEO's efficient/effective decision making. As a key component to cope with this current, we suggest the management performance analysis syystem based on Knowledge Discovery in Database (KDD). The system measures management performance that is considered with both VA(Value- Added), which represents stakeholder's point of view and EVA(Economic Value-Added), which represents shareholder's point of view. The relation ship between management performance and some 80 financial ratios is analyzed, and then important financial ratios are drawn out. In analyzing the relationship, we applied KDD process which includes such as multidimensional cube, OLAP(On-Line Analytic Process), data mining and AHP(Analytic Hierarchy Process). To demonstrate the performance of the system, we conducted a case study using financial data over the 16-years from 1981 to 1996 of Korean automobile industry which is taken from database of KISF AS(Korea Investors Services Financial Analysis System).

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Development of a Knowledge Discovery System using Hierarchical Self-Organizing Map and Fuzzy Rule Generation

  • Koo, Taehoon;Rhee, Jongtae
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2001.01a
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    • pp.431-434
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    • 2001
  • Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

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Implementation of Management performance Analysis System with Genetic Algorithms (Genetic Algorithm에 기반한 경영성과분석 시스템 구현)

  • An, Dong-Gyu;Jo, Seong-Hun
    • 한국디지털정책학회:학술대회논문집
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    • 2003.12a
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    • pp.191-210
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    • 2003
  • In modern dynamic management environment, there is growing recognition that information & knowledge management systems are essential for CEO's Efficient/effective decision making, As a key component to cope with this current, we suggest the management performance analysis system based on Knowledge Discovery in Database (KDD). The system measures management performance that is considered with both VA(Value-Added), which represents stakeholder's point of view and EVA(Economic Value-Added), which represents shareholder's point of view, The relationship between management performance and some 80 financial ratios is analyzed, and then important financial ratios are drawn out. In analyzing the relationship, we applied KDD process which includes such as multidimensional cube, OLAP(On -Line Analytic Process), data mining and AHP(Analytic Hierarchy Process). To demonstrate the performance of the system, we conducted a case study using financial data over the 16-years from 1981 to 1996 of Korean automobile industry which is taken from database of KISFAS(Korea Investors Services Financial Analysis System).

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Mathematical Foundations and Educational Methodology of Data Mining (데이터 마이닝의 수학적 배경과 교육방법론)

  • Lee Seung-Woo
    • Journal for History of Mathematics
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    • v.18 no.2
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    • pp.95-106
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    • 2005
  • This paper is investigated conception and methodology of data selection, cleaning, integration, transformation, reduction, selection and application of data mining techniques, and model evaluation during procedure of the knowledge discovery in database (KDD) based on Mathematics. Statistical role and methodology in KDD is studied as branch of Mathematics. Also, we investigate the history, mathematical background, important modeling techniques using statistics and information, practical applied field and entire examples of data mining. Also we study the differences between data mining and statistics.

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A Design of Region-Development Plan Support Processor Using Knowledge Discovery in Database (지식발견(KDD)을 응용한 지역개발계획수립 지원 프로세서의 설계)

  • 한상진;김호석;김성희;배해영
    • Proceedings of the Korean Information Science Society Conference
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    • 2004.10b
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    • pp.187-189
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    • 2004
  • 최근 정보기술의 가속적인 발전과 인터넷의 급속한 보급으로 인하여 우리는 다양하고 방대한 양의 지역정보를 접하고 이용하고 있다. 그러나 지역개발사업을 추진하는데 있어서 계획수립이 차지하는 중요성이 매우 큼에도 불구하고 지역을 대표하는 객관적이고 유용한 정보를 찾아내어 지역개발계획수립에 활용하는 예는 거의 없었다. 이에 여러 곳에 산재되어있는 지역정보들을 통합하여 관리하고 이러한 대량의 지역 데이터들로부터 지역을 특징지을 수 있는 보다 현실적이고 유용한 정보를 추출하거나 생성하여 지역정보 분석에 활용하는 방법이 필요하게 되었다. 본 논문에서는 지역개발계획을 수립하는데 있어서 방대한 양의 데이터로부터 유용한 정보를 추출하고 발견하는 지식발견(KDD : Knowledge Discovery in Database)(1) 프로세서의 전체과정에 지역개발계획 수립 목적에 맞추어 지역개발이론에 기초한 지역정보 분석과정을 삽입함으로써 보다 합리적이고 현실적인 지역개발계획이 수립되도록 지원할 수 있는 프로세서를 설계한다.

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Virus communicable disease cpidemic forecasting search using KDD and DataMining (KDD와 데이터마이닝을 이용한 바이러스성전염병 유행예측조사)

  • Yun, JongChan;Youn, SungDae
    • Proceedings of the Korea Information Processing Society Conference
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    • 2004.05a
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    • pp.47-50
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    • 2004
  • 본 논문은 대량의 데이터를 처리하는 전염병에 관한 역학조사에 대한 과정을 KDD(Knowledge Discovery in Database)와 데이터마이닝 기법을 이용해서 의료 전문인들의 지식을 데이터베이스화하여 데이터 선정, 정제, 보강, 예측과 빠른 데이터 검출을 하도록 하였다. 그리고 각 바이러스의 동향은 데이터마이닝을 활용하므로 일부분만의 데이터를 산출하지 않고 전체적인 동향을 산출, 예측하도록 한다.

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Modeling a Business Performance Information System with Knowledge Discovery in Databases (데이터베이스 지식발견체계에 기반한 경영성과 정보시스템의 구축)

  • Cho, Seong-Hoon;Chung, Min-Yong;Kim, Jong-Hwa
    • IE interfaces
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    • v.14 no.2
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    • pp.164-171
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    • 2001
  • We suggest a Business Performance Information System with Knowledge Discovery in Databases(KDD) as a key component of integrated information and knowledge management system. The proposed system measures business performance by considering both VA(Value-Added), which represents stakeholder's point of view and EVA(Economic Value-Added), which represents shareholder's point of view. In modeling of Business Performance Information System, we apply the following KDD processes : Data Warehouse for consistent management of a performance data, On-Line Analytic Processing(OLAP) for multidimensional analysis, Genetic Algorithms for exploring and finding dominant managing factors and Analytic Hierarchy Process(AHP) for applying expert's knowledge and experience. To demonstrate the performance of the system, we conducted a case study using financial data of Korean automobile industry over 16 years from 1981 to 1996, which is taken from database of KISFAS(Korea Investors Services Financial Analysis System).

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Business Performance Analysis System based on Knowledge Discovery in Databases (Knowledge Discovery in Databases에 기반한 경영성과분석 시스템)

  • 조성훈;정민용
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.23 no.57
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    • pp.11-20
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    • 2000
  • In dynamic management environment, CEO must make an efficient decision with information & knowledge management systems based on IT(Information Technology). As a key component to cope with this current, we suggest the business performance analysis system based on KDD(Knowledge Discovery in Databases). We consider the theoretical model that is composited both Value-Added in respect of stakeholder and Economic Value-Added in respect of shareholder. Additionally we use DBMS and data mining method using Genetic Algorithms as physical model. To demonstrate the performance of the business performance analysis system, we analyse a domestic motors industry. The empirical case is based on the financial data of KISFAS(Korea Investors Services Financial Analysis System) database. The samples included in the study consist of H motors/S motors industry over the 16-year from 1981 to 1996.

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ICAIM;An Improved CAIM Algorithm for Knowledge Discovery

  • Yaowapanee, Piriya;Pinngern, Ouen
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.2029-2032
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    • 2004
  • The quantity of data were rapidly increased recently and caused the data overwhelming. This led to be difficult in searching the required data. The method of eliminating redundant data was needed. One of the efficient methods was Knowledge Discovery in Database (KDD). Generally data can be separate into 2 cases, continuous data and discrete data. This paper describes algorithm that transforms continuous attributes into discrete ones. We present an Improved Class Attribute Interdependence Maximization (ICAIM), which designed to work with supervised data, for discretized process. The algorithm does not require user to predefine the number of intervals. ICAIM improved CAIM by using significant test to determine which interval should be merged to one interval. Our goal is to generate a minimal number of discrete intervals and improve accuracy for classified class. We used iris plant dataset (IRIS) to test this algorithm compare with CAIM algorithm.

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An Efficient Knowledge Base Management Using Hybrid SOM (하이브리드 SOM을 이용한 효율적인 지식 베이스 관리)

  • Yoon, Kyung-Bae;Choi, Jun-Hyeog;Wang, Chang-Jong
    • The KIPS Transactions:PartB
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    • v.9B no.5
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    • pp.635-642
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    • 2002
  • There is a rapidly growing demand for the intellectualization of information technology. Especially, in the area of KDD (Knowledge Discovery in Database) which should make an optimal decision of finding knowledge from a large amount of data, the demand is enormous. A large volume of Knowledge Base should be efficiently managed for a more intellectual choice. This study is proposing a Hybrid SOM for an efficient search and renewal of knowledge base, which combines a self-study nerve network, Self-Organization Map with a probable distribution theory in order to get knowledge needed for decision-making management from the Knowledge Base. The efficient knowledge base management through this proposed method is carried out by a stimulation test. This test confirmed that the proposed Hybrid SOM can manage with efficiency Knowledge Base.