• Journal of Information Processing Systems
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• 제19권2호
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• 한국경영과학회지
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• 제13권1호
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• pp.31-38
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• 1988

This paper presents a methodology for the development of a part classification and coding system suited to each individual company. When coding a group of parts for a specific company by a general purpose part classification & coding system like OPITZ system, it is frequently observed that we use only a small subset of total available code numbers. Such sparsity in the actual occurrences of code numbers implies that we can design a better system which uses digits of the system more parsimoniously. A 2-dimensional fuzzy ISODATA algorithm is developed to extract the important characteristics for the classification from the set of given parts. Based on the extracted characteristics nd the distances between fuzzy clustering cenetroids, a company-unique classification and coding system can be developed. An example case study for a medium sized machine shop is presented.

• 한국컴퓨터정보학회논문지
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• 제24권2호
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• pp.127-137
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• 2019

In this study, we have developed a calculation system for BIM-based quantities, 4D process, and 5D construction costs, by integrating object shape attributes and the standard classification system which consist of Cost Breakdown System(CBS), Object Breakdown System(OBS) and Work Breakdown System(WBS) in order to use for the 4 dimensional process control of roads and rivers. First, a new BIM library database connected with the BIM library shape objects was built according to the CBS/OBS/WBS standard classification system of the civil engineering field, and a integrated database system of BIM-based quantities, process(4D), and construction costs(5D) for roads and rivers was constructed. Nextly, the process classification system and the cost classification system were automatically disassembled to the BIM objects consisting of the Revit-family style elements. Finally, we added functions for automatically generating four dimensional activities and generating a automatic cost statement according to the combination of WBS and CBS classification system The ultimate goal of this study was to extend the integrated quantities, process(4D), and construction costs(5D) system for new roads and rivers, enabling the integrated use of process(4D) and construction costs(5D) in the design and construction stage, based on the tasks described above.

• 통상정보연구
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• 제6권3호
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• pp.41-61
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• 2004

Today, most companies are needed to become interested on e-Biz and information management system. Especially, Data classification format system was very important for application to effective and efficiency management decision support. They should include main entry which consists of department, employee's name, title, publication date. Now, each company is using eleven different methods on data classification format system. In this paper finding result was as follows, in other words, general management document case using the nine date classification methods and special report management document ca se using the twodata classification methods. The aim of this study is to investigate problems that the present data classification format system has and some concerns that should be taken into account in case of the modification of the data classification system and change into a new one. This study is based on the survey in that the company managergave to 35 companies throughout the nation. As a result, the survey indicates that the crucial concerns of the participating managers are ineffective management information source and the duplication of data classification systems. This paper is the transcendental study the introduction of data classification format systems to business companies in Korea. This paper provided the fundamental data for the effective business process reengineering in business activity for management information.

• 한국건축시공학회지
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• 제8권1호
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• pp.63-70
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• 2008

Management process of apartment buildings construction has increased because the after service of construction company meet the needs of customers. Many defect data, which was inspected by construction company or customers before moving into a new apartment house, were classified by field engineers and then communicated to corresponding subcontractors. The classification process needs to be performed by an expert engineer because there is so much data, it is unfeasible to complete in a short period of time. For this classification process, an automatic classification system using case base reasoning (CBR) should be considered. This research proposed a defect management system with automatic classification system using CBR. This constructed defect management system consists of cyber after service system for tenants and the whole defect management process of construction, preservation and management of apartment buildings. This system could improve the efficiency of expert work in terms of time and accuracy, as well as helping laymen users to conduct defect classification work as experts do.

• 한국지능시스템학회논문지
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• 제26권3호
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• pp.190-195
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• 2016

급변하는 글로벌 시장 환경에서 기술은 계속해서 급속히 발전하고 있다. 이러한 급변하고 있는 환경을 반영한 연구개발은 기업에 있어서 필수가 되었다. 즉, 기업의 경쟁력 향상을 위해서는 자사가 보유한 기술에 대한 체계적인 분석이 필요하다. 최근에는 객관적이며 정량화된 기술분류를 위하여 특허문서의 IPC 코드를 이용하여 기술분류를 수행하고 있다. 국제특허분류인 IPC 코드는 국제적으로 규격화된 기술분류 코드이기 때문에, 이를 활용하면 객관적이고 정량화된 기술분석 수행이 가능하다. 본 논문에서는 C사의(社) 특허에 대하여 전수조사를 실시하고, IPC 코드기반 분석 Matrix를 구축한 후 해당특허들을 신뢰도 기반의 연관규칙 마이닝을 실시하며 구조화된 연관맵을 생성한다. 연관맵을 이용하면 해당회사의 특허 현황 파악에 유용하게 활용된다. 또한, 구조화된 연관맵을 이용하면 상호 연관있는 기술에 대하여 군집화를 가능하게 하기 때문에, 본 논문에서 제시한 C사(社)의 기술을 파악할 수 있으며 이를 기반으로 기술 흐름과 향후 기술 전략 수립을 가능하게 한다.

• 대한원격탐사학회지
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• 제18권3호
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• pp.163-169
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• 2002

The land uses of Korean peninsula are very complicated and high-density. Therefore, the image classification using coarse resolution satellite images may not provide good results for the land cover classification. The purpose of this paper is to compare the classification accuracy of visual interpretation with that of digital image classification of satellite remote sensing data such as 20m SPOT and 30m TM. In this study, hybrid classification was used. Classification accuracy was assessed by comparing each classification result with reference data obtained from KOMPSAT-1 EOC imagery, air photos, and field surveys.

• 산업공학
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• 제18권1호
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• pp.10-21
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• 2005

With the increasing proliferation of World Wide Web, electronic mail systems have become very widely used communication tools. Researches on e-mail classification have been very important in that e-mail classification system is a major engine for e-mail response management systems which mine unstructured e-mail messages and automatically categorize them. In this research we compare the performance of Naive Bayesian learning and Centroid-Based Classification using the different data set of an on-line shopping mall and a credit card company. We analyze which method performs better under which conditions. We compared classification accuracy of them which depends on structure and size of train set and increasing numbers of class. The experimental results indicate that Naive Bayesian learning performs better, while Centroid-Based Classification is more robust in terms of classification accuracy.

• 디지털산업정보학회논문지
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• 제15권4호
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.

• 한국IT서비스학회지
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• 제2권2호
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• pp.87-95
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• 2003

With the increasing proliferation of World Wide Web, electronic mail systems have become very widely used communication tools. Researches on e-mail classification have been very important in that e-mail classification system is a major engine for e-mail response management systems which mine unstructured e-mail messages and automatically categorize them. in this research we develop e-mail classifiers for e-mail Response Management Systems (ERMS) using naive bayesian learning and centroid-based classification. We analyze which method performs better under which conditions, comparing classification accuracies which may depend on the structure, the size of training data set and number of classes, using the different data set of an on-line shopping mall and a credit card company. The developed e-mail classifiers have been successfully implemented in practice. The experimental results show that naive bayesian learning performs better, while centroid-based classification is more robust in terms of classification accuracy.