• Title/Summary/Keyword: Data Mining

Search Result 2,192, Processing Time 0.236 seconds

Feature Selection Methodology in Quality Data Mining

  • Soo, Nam-Ho;Halim, Yulius
    • Proceedings of the Korean Operations and Management Science Society Conference
    • /
    • 2004.05a
    • /
    • pp.698-701
    • /
    • 2004
  • In many literatures, data mining has been used as a utilization of data warehouse and data collection. The biggest utilizations of data mining are for marketing and researches. This is solely because of the data available for this field is usually in large amount. The usability of the data mining is expandable also to the production process. While the object of research of the data mining in marketing is the customers and products, data mining in the production field is object to the so called 4MlE, man, machine, materials, method (recipe) and environment. All of the elements are important to the production process which determines the quality of the product. Because the final aim of the data mining in production field is the quality of the production, this data mining is commonly recognized as quality data mining. As the variables researched in quality data mining can be hundreds or more, it could take a long time to reveal the information from the data warehouse. Feature selection methodology is proposed to help the research take the best performance in a relatively short time. The usage of available simple statistical tools in this method can help the speed of the mining.

  • PDF

Extraction of Expert Knowledge Based on Hybrid Data Mining Mechanism (하이브리드 데이터마이닝 메커니즘에 기반한 전문가 지식 추출)

  • Kim, Jin-Sung
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.14 no.6
    • /
    • pp.764-770
    • /
    • 2004
  • This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

Development of Data Mining Tool Using S-PLUS and StatServer (S-PLUS와 StatServer를 이용한 Data Mining 도구 개발)

  • 정인석;이재준
    • Journal of Intelligence and Information Systems
    • /
    • v.4 no.2
    • /
    • pp.129-139
    • /
    • 1998
  • 통계 software에는 data mining에 필요한 다양한 모형과 함수들이 제공되고 있어 이를 이용한 data mining 도구가 소개되고 있다. 본 논문에서는 data mining을 수행하는데 효과적인 환경을 제공하는 S-Plus로 data mining 기법들을 구현하거나 재구성하였으며, StatServer를 이용하여 대용량의 data base를 직접 관리할 수 있게 하고, S-PLUS의 분석기능을 Internet을 통하여 사용할 수 있게 하여 원거리에서 data mining작업을 수행될 수 있도록 구성하였다. 또한 분석자는 찾아낸 모형을 복잡한 프로그래밍 작업 없이 새로운 웹 페이지를 만들 수 있으며, 이를 통해 운영계의 사용자가 최적 모형이 제시하는 결과를 실제 업무에 즉시 이용할 수 있도록 하였다.

  • PDF

A Study on the Development of Internet Purchase Support Systems Based on Data Mining and Case-Based Reasoning (데이터마이닝과 사례기반추론 기법에 기반한 인터넷 구매지원 시스템 구축에 관한 연구)

  • 김진성
    • Journal of the Korean Operations Research and Management Science Society
    • /
    • v.28 no.3
    • /
    • pp.135-148
    • /
    • 2003
  • In this paper we introduce the Internet-based purchase support systems using data mining and case-based reasoning (CBR). Internet Business activity that involves the end user is undergoing a significant revolution. The ability to track users browsing behavior has brought the vendor and end customer's closer than ever before. It is now possible for a vendor to personalize his product message for individual customers at massive scale. Most of former researchers, in this research arena, used data mining techniques to pursue the customer's future behavior and to improve the frequency of repurchase. The area of data mining can be defined as efficiently discovering association rules from large collections of data. However, the basic association rule-based data mining technique was not flexible. If there were no inference rules to track the customer's future behavior, association rule-based data mining systems may not present more information. To resolve this problem, we combined association rule-based data mining with CBR mechanism. CBR is used in reasoning for customer's preference searching and training through the cases. Data mining and CBR-based hybrid purchase support mechanism can reflect both association rule-based logical inference and case-based information reuse. A Web-log data gathered in the real-world Internet shopping mall is given to illustrate the quality of the proposed systems.

Data Mining for High Dimensional Data in Drug Discovery and Development

  • Lee, Kwan R.;Park, Daniel C.;Lin, Xiwu;Eslava, Sergio
    • Genomics & Informatics
    • /
    • v.1 no.2
    • /
    • pp.65-74
    • /
    • 2003
  • Data mining differs primarily from traditional data analysis on an important dimension, namely the scale of the data. That is the reason why not only statistical but also computer science principles are needed to extract information from large data sets. In this paper we briefly review data mining, its characteristics, typical data mining algorithms, and potential and ongoing applications of data mining at biopharmaceutical industries. The distinguishing characteristics of data mining lie in its understandability, scalability, its problem driven nature, and its analysis of retrospective or observational data in contrast to experimentally designed data. At a high level one can identify three types of problems for which data mining is useful: description, prediction and search. Brief review of data mining algorithms include decision trees and rules, nonlinear classification methods, memory-based methods, model-based clustering, and graphical dependency models. Application areas covered are discovery compound libraries, clinical trial and disease management data, genomics and proteomics, structural databases for candidate drug compounds, and other applications of pharmaceutical relevance.

Design and Implementation of a Data Mining Query Processor (데이터 마이닝 질의 처리를 위한 질의 처리기 설계 및 구현)

  • Kim, Chung-Seok;Kim, Kyung-Chang
    • The KIPS Transactions:PartD
    • /
    • v.8D no.2
    • /
    • pp.117-124
    • /
    • 2001
  • A data mining system includes various data mining functions such as aggregation, association and classification, among others. To express these data mining function, a powerful data mining query language is needed. In addition, a graphic user interface(GUI) based on the data mining query language is needed for users. In addition, processing a data mining query targeted for a data warehouse, which is the appropriate data repository for decision making, is needed. In this paper, we first build a GUI to enable users to easily define data mining queries. We then propose a data mining query processing framework that can be used to process a data mining query targeted for a data warehouse. We also implement a schema generate a data warehouse schema that is needed to build a data warehouse. Lastly, we show the implementation details of a query processor that can process queries that discover association rules.

  • PDF

Data Mining for Strategy focused CRM Structure (전략중심의 CRM구조의 데이터마이닝)

  • Yoon Yong W.
    • Proceedings of the Korean Operations and Management Science Society Conference
    • /
    • 2004.10a
    • /
    • pp.399-405
    • /
    • 2004
  • With the explosive growth of information sources available under various information technology and business environment, it has become increasingly necessary for determining effective marketing strategies and optimizing the logical structure of the CRM data mining system. In this paper, we present an overview of the data mining for strategy focused CRM structure. This includes preprocessing, transaction identification and data integration components. We describe the main part of this paper to the discussion of processes and problems that characterize the mining tools and techniques, identify the CRM data mining, and provide a general architecture of a system to do focused CRM data mining that require further research and development.

  • PDF

A Study on the Hybrid Data Mining Mechanism Based on Association Rules and Fuzzy Neural Networks (연관규칙과 퍼지 인공신경망에 기반한 하이브리드 데이터마이닝 메커니즘에 관한 연구)

  • Kim Jin Sung
    • Proceedings of the Korean Operations and Management Science Society Conference
    • /
    • 2003.05a
    • /
    • pp.884-888
    • /
    • 2003
  • In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

  • PDF

Directed Association Rules Mining and Classification (목표 속성을 고려한 연관규칙과 분류 기법)

  • 한경록;김재련
    • Journal of the Society of Korea Industrial and Systems Engineering
    • /
    • v.24 no.63
    • /
    • pp.23-31
    • /
    • 2001
  • Data mining can be either directed or undirected. One way of thinking about it is that we use undirected data mining to recognize relationship in the data and directed data mining to explain those relationships once they have been found. Several data mining techniques have received considerable research attention. In this paper, we propose an algorithm for discovering association rules as directed data mining and applying them to classification. In the first phase, we find frequent closed itemsets and association rules. After this phase, we construct the decision trees using discovered association rules. The algorithm can be applicable to customer relationship management.

  • PDF

Receiver Operating Characteristic Analysis by Data Mining

  • Rhee Seong-Won;Lee Jea-Young
    • Proceedings of the Korean Statistical Society Conference
    • /
    • 2001.11a
    • /
    • pp.195-197
    • /
    • 2001
  • Data Mining is used to discover patterns and relationships in huge amounts of data. Researchers in many different fields have shown great interest in data mining analysis. Using the classification technique of data mining analysis, the available model for Receiver Operating Characteristic(ROC) method is presented. We present that this may help analyze result of data mining techniques.

  • PDF