• Title/Summary/Keyword: logical rules

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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
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    • 2003.05a
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    • pp.884-888
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    • 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.

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Learning Rules for Partially Occluded Object Recognition (부분적으로 가려진 물체의 인식 룰의 습득)

  • 정재영;김문현
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.27 no.6
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    • pp.954-962
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    • 1990
  • Experties of recognizing an object despite of every possible occlusions among objects is difficult to be provided directly to a system. In this paper, we propose a method for inferring inherent shape-characteirstics of an object from training views provided. The method learns rules incrementally by alternating the rule induction process from limited number of training views and the rule verification process from the following taining views. The learned rules are represented using logical expressions to enhance the readability. Thr proposed method is tested by simulating occlusions on 2-dimensional objects to examine the learning process and to show improvement of recognition rate. Thr result shows that it can be applied to a practical system for 3-dimensional object recognition.

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Secondary School Science Teachers' Emotional Display Rules and Emotional Labor Types (중등 과학교사의 감정표현규칙과 감정노동 유형)

  • Kim, Heekyong
    • Journal of The Korean Association For Science Education
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    • v.37 no.4
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    • pp.705-717
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    • 2017
  • The purpose of the study is to explore secondary science teachers' emotional display rules, types of emotional labor, science-specific emotional display rules and the episodes of emotional labor. For this purpose, the survey to measure emotional labor of science teachers (The Emotional Labor of Science Teaching Scale: TELSTS) was developed and the participants were 145 secondary science teachers in Korea. Results showed that first, secondary science teachers recognized the emotional display rules defined by their schools, especially, positive display rules. Second, secondary science teachers showed that they were carrying out emotional labor in order to keep their emotional display rules in check. The mean value of responses to deep acting was high. Also, there were statistically significant differences in emotional labor depending on whether they were full-time or part-time teachers and their teaching career. Third, as a result of analyzing the specificity of science teachers, it was mainly related to the objective and logical image of science, and experimental instruction. Seventy-four percent (74%) of responses were negative or neutral emotional display rules. Finally, implications for science education are discussed.

Hybrid Intelligent Web Recommendation Systems Based on Web Data Mining and Case-Based Reasoning

  • Kim, Jin-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.3
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    • pp.366-370
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    • 2003
  • In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers' preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can't present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

Dynamic Text Categorizing Method using Text Mining and Association Rule

  • Kim, Young-Wook;Kim, Ki-Hyun;Lee, Hong-Chul
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.10
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    • pp.103-109
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    • 2018
  • In this paper, we propose a dynamic document classification method which breaks away from existing document classification method with artificial categorization rules focusing on suppliers and has changing categorization rules according to users' needs or social trends. The core of this dynamic document classification method lies in the fact that it creates classification criteria real-time by using topic modeling techniques without standardized category rules, which does not force users to use unnecessary frames. In addition, it can also search the details through the relevance analysis by calculating the relationship between the words that is difficult to grasp by word frequency alone. Rather than for logical and systematic documents, this method proposed can be used more effectively for situation analysis and retrieving information of unstructured data which do not fit the category of existing classification such as VOC (Voice Of Customer), SNS and customer reviews of Internet shopping malls and it can react to users' needs flexibly. In addition, it has no process of selecting the classification rules by the suppliers and in case there is a misclassification, it requires no manual work, which reduces unnecessary workload.

An Extension of SWCL to Represent Logical Implication Knowledge under Semantic Web Environment (의미웹 환경에서 조건부함축 제약 지식표현을 위한 SWCL의 확장)

  • Kim, Hak-Jin
    • Journal of the Korean Operations Research and Management Science Society
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    • v.39 no.3
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    • pp.7-22
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    • 2014
  • By the publications of RDF and OWL, the Semantic Web is confirmed as a technology through which information in the Internet can be processed by machines. The focus of the Semantic Web study after then has moved to how to provide more useful information to users for their decision making beyond simple use of the structured data in ontologies. SWRL that makes logical inference possible by rules, and SWCL that formulates constraints under the Semantic Web environment are some of many efforts toward the achievement of that goal. Constraint represents a connection or a relationship between individual data in ontology. Based on SWCL, this paper tries to extend the language by adding one more type of constraint, implication constaint, in its repertoire. When users use binary variables to represent logical relationships in mathematical models, it requires and knowledge on the solver to solve the models. The use of implication constraint ease this difficulty. Its need, definition and relevant technical description is presented by the use of the optimal common attribute selection problem in product design.

Advanced Logical Non-Linear Torque Sharing Function for Torque Ripple Reduction of SRM (SRM의 토크리플 저감을 위한 개선된 논리적 비선형 토크분배기법)

  • Kim, Tae-Hyoung;Lee, Dong-Hee;Ahn, Jin-Woo
    • Proceedings of the KIEE Conference
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    • 2009.07a
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    • pp.887_888
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    • 2009
  • In this paper, an advanced torque control scheme of Switched Reluctance Motor (SRM) using modified non-linear logical TSF (Torque Sharing Function) based on the DITC (direct instantaneous torque control) with PWM(Pulse Width Modulation). In the proposed control scheme, a simple calculation of PWM duty ratio, switching rules from DITC and non-linear torque sharing function can reduce the torque ripple with fixed switching frequency. The proposed control scheme is verified by the computer simulations and experimental results.

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Extracting Rules from Neural Networks with Continuous Attributes (연속형 속성을 갖는 인공 신경망의 규칙 추출)

  • Jagvaral, Batselem;Lee, Wan-Gon;Jeon, Myung-joong;Park, Hyun-Kyu;Park, Young-Tack
    • Journal of KIISE
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    • v.45 no.1
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    • pp.22-29
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    • 2018
  • Over the decades, neural networks have been successfully used in numerous applications from speech recognition to image classification. However, these neural networks cannot explain their results and one needs to know how and why a specific conclusion was drawn. Most studies focus on extracting binary rules from neural networks, which is often impractical to do, since data sets used for machine learning applications contain continuous values. To fill the gap, this paper presents an algorithm to extract logic rules from a trained neural network for data with continuous attributes. It uses hyperplane-based linear classifiers to extract rules with numeric values from trained weights between input and hidden layers and then combines these classifiers with binary rules learned from hidden and output layers to form non-linear classification rules. Experiments with different datasets show that the proposed approach can accurately extract logical rules for data with nonlinear continuous attributes.

Bitmap Intersection Lookup (BIL);A Packet Classification's Algorithm with Rules Updating

  • Khunkitti, Akharin;Promrit, Nuttachot
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.767-772
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    • 2005
  • The Internet is a packet switched network which offers best-effort service, but current IP network provide enhanced services such Quality of Services, Virtual Private Network (VPN) services, Distribute Firewall and IP Security Gateways. All such services need packet classification for determining the flow. The problem is performing scalable packet classification at wire speeds even as rule databases increase in size. Therefore, this research offer packet classification algorithm that increase classifier performance when working with enlarge rules database by rearrange rule structure into Bitmap Intersection Lookup (BIL) tables. It will use packet's header field for looking up BIL tables and take the result with intersection operation by logical AND. This approach will use simple algorithm and rule structure, it make classifier have high search speed and fast updates.

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Knowledge Extractions, Visualizations, and Inference from the big Data in Healthcare and Medical

  • Kim, Jin Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.23 no.5
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    • pp.400-405
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    • 2013
  • The purpose of this study is to develop a composite platform for knowledge extractions, visualizations, and inference. Generally, the big data sets were frequently used in the healthcare and medical area. To help the knowledge managers/users working in the field, this study is focused on knowledge management (KM) based on Data Mining (DM), Knowledge Distribution Map (KDM), Decision Tree (DT), RDBMS, and SQL-inference. The proposed mechanism is composed of five key processes. Firstly, in Knowledge Parsing, it extracts logical rules from a big data set by using DM technology. Then it transforms the rules into RDB tables. Secondly, through Knowledge Maintenance, it refines and manages the knowledge to be ready for the computing of knowledge distributions. Thirdly, in Knowledge Distribution process, we can see the knowledge distributions by using the DT mechanism.Fourthly, in Knowledge Hierarchy, the platform shows the hierarchy of the knowledge. Finally, in Inference, it deduce the conclusions by using the given facts and data.This approach presents the advantages of diversity in knowledge representations and inference to improve the quality of computer-based medical diagnosis.