• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.111-121
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• 2014

In this paper, we propose tree-based hybrid query tree architecture utilizing time slot. 4-Bit Pattern Slot Allocation(4-SL) has a 8-ary tree structure and when tag ID responses according to query of the reader, it applies a digital coding method, the Manchester code, in order to extract the location and the number of collided bits. Also, this algorithm can recognize multiple Tags by single query using 4 fixed time slots. The architecture allows the reader to identify 8 tags at the same time by responding 4 time slots utilizing the first bit($[prefix+1]^{th}$, F ${\in}$ {'0' or '1'}) and bit pattern from second ~ third bits($[prefix+2]^{th}{\sim}[prefix+3]^{th}$, $B_2{\in}$ {"00" or "11"}, $B_1{\in}$ {"01" or "10"}) in tag ID. we analyze worst case of the number of query nodes(prefix) in algorithm to extract delay time for recognizing multiple tags. The identification delay time of the proposed algorithm was based on the number of query-responses and query bits, and was calculated by each algorithm.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.10
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• pp.59-68
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• 1998

The goal of data mining is to develop the automatic and intelligent tools and technologies that can find useful knowledge from databases. To meet this goal, we propose an efficient data mining algorithm based on the fuzzy decision tree. The proposed method combines comprehensibility of decision tree such as ID3 and C4.5 and representation power of fuzzy set theory. So, it can generate simple and comprehensive rules describing data. The proposed algorithm consists of two stages: the first stage generates the fuzzy membership functions using histogram analysis, and the second stage constructs a fuzzy decision tree using the fuzzy membership functions. From the testing of the proposed algorithm on the IRIS data and the Wisconsin Breast Cancer data, we found that the proposed method can generate a set of fuzzy rules from data efficiently.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.802-805
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• 2008

RFID 시스템에서 리더와 태그는 단일 무선 공유 채널을 갖기 때문에 RFID 수동형 태그를 위한 태그 충돌 중재가 태그 인식을 위한 중요한 이슈이다. 본 논문에서는 태그 충돌 방지를 위한 Improved Hybrid Query Tree algorithm 을 제안한다. 제안된 알고리즘은 쿼리 트리를 기반으로 태그가 리더에게 ID 를 전송하는 시점을 전송 ID 상위 3 비트 내의 '1' 값을 이용하여 결정한다. 또한 전송받은 Tag 의 상위 3 비트는 충돌이 발생하더라도 전송 슬롯에 따라 다르므로 제안한 알고리즘에서 예측이 가능하다. 시뮬레이션을 통한 성능 평가에서 다른 트리 기반 프로토콜에 비해 제안한 알고리즘이 쿼리 횟수에서 높은 성능을 갖는다는 것을 보여준다.

• The Journal of Information Technology and Database
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• v.7 no.2
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• pp.17-32
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• 2000

As XML emerging as the Internet electronic document language standard of the next generation, the number of XML documents which contain vast amount of Information is increasing substantially through the transformation of existing documents to XML documents or the appearance of new XML documents. Consequently, XML document retrieval system becomes extremely essential for searching through a large quantity of XML documents that are storied in and managed by DBMS. In this paper we describe the design and implementation of BADA-IV/XML query processor that supports content-based, structure-based and attribute-based retrieval. We design XML query language based upon XQL (XML Query Language) of W3C and tightly-coupled with OQL (a query language for object-oriented database). XML document is stored and maintained in BADA-IV, which is an object-oriented database management system developed by ETRI (Electronics and Telecommunications Research Institute) The storage data model is based on DOM (Document Object Model), therefore the retrieval of XML documents is executed basically using DOM tree traversal. We improve the search performance using Node ID which represents node's hierarchy information in an XML document. Assuming that DOW tree is a complete k-ary tree, we show that Node ID technique is superior to DOM tree traversal from the viewpoint of node fetch counts.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.102-108
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• 2005

This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.219-221
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• 2022

Seoul Metropolitan City, the world's top 10 cities and Metro City, has traditional urban manufacturing industries such as printing, sewing, and mechanical metals. These manufacturing industries have developed in the form of mutual assistance by forming small business clusters according to detailed industries and processes. Due to the nature of the cluster, logistics between companies for each process in the cluster are being carried out quickly, but it is difficult for relatively small small business owners to prepare order processing services for consumers of finished products. Therefore, it is urgent to introduce an integrated order fulfillment service platform for collective business owners for smooth order and delivery processing. In this paper, we collect and analyze the existing Fulfillment Service data of small business owners in the printing industry among traditional urban industries, and design an artificial intelligence Fulfillment Service Platform system applying CRNN, k-NN, and ID3 Decision Tree algorithm. Through this study, it is expected to greatly contribute to the increase in sales and capacity of small business owners by enabling the use of individual orders and customized delivery services that can be used by any small business owner in the cluster.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1314-1323
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• 1999

컴퓨터의 사용이 일반화됨에 따라 데이타를 생성하고 수집하는 것이 용이해졌다. 이에 따라 데이타로부터 자동적으로 유용한 지식을 얻는 기술이 필요하게 되었다. 데이타 마이닝에서 얻어진 지식은 정확성과 이해성을 충족해야 한다. 본 논문에서는 데이타 마이닝을 위하여 퍼지 결정트리에 기반한 효율적인 퍼지 규칙을 생성하는 알고리즘을 제안한다. 퍼지 결정트리는 ID3와 C4.5의 이해성과 퍼지이론의 추론과 표현력을 결합한 방법이다. 특히, 퍼지 규칙은 속성 축에 평행하게 판단 경계선을 결정하는 방법으로는 어려운 속성 축에 평행하지 않는 경계선을 갖는 패턴을 효율적으로 분류한다. 제안된 알고리즘은 첫째, 각 속성 데이타의 히스토그램 분석을 통해 적절한 소속함수를 생성한다. 둘째, 주어진 소속함수를 바탕으로 ID3와 C4.5와 유사한 방법으로 퍼지 결정트리를 생성한다. 또한, 유전자 알고리즘을 이용하여 소속함수를 조율한다. IRIS 데이타, Wisconsin breast cancer 데이타, credit screening 데이타 등 벤치마크 데이타들에 대한 실험 결과 제안된 방법이 C4.5 방법을 포함한 다른 방법보다 성능과 규칙의 이해성에서 보다 효율적임을 보인다.Abstract With an extended use of computers, we can easily generate and collect data. There is a need to acquire useful knowledge from data automatically. In data mining the acquired knowledge needs to be both accurate and comprehensible. In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for data mining. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets. Particularly, fuzzy rules allow us to effectively classify patterns of non-axis-parallel decision boundaries, which are difficult to do using attribute-based classification methods.In our algorithm we first determine an appropriate set of membership functions for each attribute of data using histogram analysis. Given a set of membership functions then we construct a fuzzy decision tree in a similar way to that of ID3 and C4.5. We also apply genetic algorithm to tune the initial set of membership functions. We have experimented our algorithm with several benchmark data sets including the IRIS data, the Wisconsin breast cancer data, and the credit screening data. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with other methods including C4.5.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.62-67
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• 2011

This study has been undertaken to empirically test how the level of information Home Helpers have about their client and the hypothesis that home-helpers' expertise about their service provided to their clients is expected to positively affect their assessment of service provided to clients. Main results of this study are as follows : First, home-helpers' knowledge, skill, and information about clients is positively related to their assessment of service provided to clients. Home-helpers' information about clients plays a more important role than knowledge and skills in the process of assessing the services provided to clients. After maintaining that the hypothesis is empirically confirmed, the author discusses the implications of empirical findings.

• Journal of Information Technology Applications and Management
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• v.18 no.3
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• pp.91-110
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• 2011

Korea professional baseball industry has grown to take the lion's share of the domestic sports industry, but still does not make break even. The purpose of this study is to examine the financial impact of adopting the Customer Relation Management (CRM) approach on the profitability of Korea professional baseball industry. We use a measuring tool called entropy used in ID3 decision tree algorithm. In the paper, we specify five the most important factors that affect spectator satisfaction based on the previous literature, perform survey analysis, calculate entropy values, and find the results. We predicted the change in revenues when we adopt CRM by checking the spectators' willingness to pay more when the conditions of each factor are improved. We find that we can reap significant fruits of the effect of CRM introduction through enhancing 'game content factor' and 'game promotion factor' among the five factors. We also find that we can increase the revenues of domestic professional baseball teams to 2.4 times or 2.1 times the current level if we manage intensively those two factors respectively. It is very surprising to see that the improvement in total revenues makes both ends meet for domestic professional baseball teams. This clearly demonstrates the effectiveness of CRM approach in improving the profitability of organizations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.165-169
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• 2010

This study seeks to find the factors associated with social workers' turnover intention and show us how to manage turnovers by looking for some rules affecting turnover intentions. Our investigation surveying 331 social workers reveals that social workers' turnover intentions are affected by organizational commitment, job satisfaction, and burnout. Our pattern analyses using fuzzy ID3 show that the higher their commitment, the higher their job satisfaction stemming from promotion opportunities, rewards, and personal relations with peers and bosses. In addition, turnover intentions decreases (even if burnouts--the job-related stress--are very serious) when organizational commitment increases. We come to understand that organizational commitment could be a more important variable than job satisfaction and burnouts. Such results suggest that it would be necessary to consider how to improve social workers' organization-wide commitment rather than satisfaction and burnout related to jobs and environments.