• Journal of Digital Convergence
• /
• v.15 no.4
• /
• pp.191-198
• /
• 2017

It is needed to support intelligent customized health information service for user convenience in PHR based Personal Health Care Service Platform. In this paper, we specify an ontology-based health data model for Personal Health Care Service Platform. We also design a knowledge search algorithm that can be used to figure out similar health record by applying machine learning and data mining techniques. Axis-based mining algorithm, which we proposed, can be performed based on axis-attributes in order to improve relevance of knowledge exploration and to provide efficient search time by reducing the size of candidate item set. And K-Nearest Neighbor algorithm is used to perform to do grouping users byaccording to the similarity of the user profile. These algorithms improves the efficiency of customized information exploration according to the user 's disease and health condition. It can be useful to apply the proposed algorithm to a process of inference in the Personal Health Care Service Platform and makes it possible to recommend customized health information to the user. It is useful for people to manage smart health care in aging society.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.2 s.40
• /
• pp.43-52
• /
• 2006

LRecently, with increasing interest in ubiquitous computing, knowledge discovery method is needed with consideration of the efficiency and the effectiveness of wide range and various forms of data. Spatial Characterization which extends former characterization method with consideration of spatial and non-spatial property enables to find various form of knowledge in spatial region. The previous spatial characterization methods have the problems as follows. Firstly, former study shows the problem that the result of searched knowledge is unable to perform the multiple spatial analysis. Secondly, it is unable to secure the useful knowledge search since it searches the limited spatial region which is allocated by the user. Thus, this study suggests spatial characterization which applies to density based clustering.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.5
• /
• pp.1095-1106
• /
• 2014

Symbolic data analysis extends the data mining and exploratory data analysis to the knowledge mining, we can suggest the SDA tree model on clinical and genomic data with new knowledge mining SDA approach. Using SDA application for huge genomic SNP data, we can get the correlation the availability of understanding of hidden structure of HCC data could be proved. We can confirm validity of application of SDA to the tree structured progression model and to quantify the clinical lab data and SNP data for early diagnosis of HCC. Our proposed model constructs the representative model for HCC survival time and causal association with their SNP gene data. To fit the simple and easy interpretation tree structured survival model which could reduced from huge clinical and genomic data under the new statistical theory of knowledge mining with SDA.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.1 s.39
• /
• pp.11-18
• /
• 2006

The logical analysis of data(LAD) is a Boolean-logic based data mining tool. A critical step in analyzing data by LAD is the pattern generation stage where useful knowledge and hidden structural information in data is discovered in the form of patterns. A conventional method for pattern generation in LAD is based on term enumeration that renders the generation of higher degree patterns practically impossible. In this paper, we present a novel optimization-based pattern generation methodology and propose two mathematical programming models, a mixed 0-1 integer and linear programming (MILP) formulation and a well-studied set covering problem (SCP) formulation for the generation of optimal and heuristic patterns, respectively. With benchmark datasets, we demonstrate the effectiveness of our models by automatically generating with ease patterns of high complexity that cannot be generated with the conventional approach.

• Journal of Fashion Business
• /
• v.27 no.3
• /
• pp.50-62
• /
• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• Journal of Korea Multimedia Society
• /
• v.11 no.11
• /
• pp.1566-1574
• /
• 2008

Researches of current information searching focus on providing personalized results as well as matching needed queries in an enormous amount of information. This paper aims at discovering hidden knowledge to provide personalized and inferred search results based on the ontology with categorized concepts and relations among data. The current searching occasionally presents too much redundant information or offers no matching results from large volumes of data. To lessen this disadvantages in the information searching, we propose an inference algorithm that supports associated and inferred searching through the Jess engine based on the OWL ontology constraints and knowledge expressed by SWRL with association rules. After constructing the personalized preference ontology for domains such as restaurants, gas stations, bakeries, and so on, it shows that new knowledge information generated from the ontology and the rules is provided with an example of the domain of gas stations.

• Journal of Information Processing Systems
• /
• v.9 no.3
• /
• pp.435-460
• /
• 2013

Recommender systems are popular applications that help users to identify items that they could be interested in. A recent research area on recommender systems focuses on detecting several kinds of inconsistencies associated with the user preferences. However, the majority of previous works in this direction just process anomalies that are intentionally introduced by users. In contrast, this paper is centered on finding the way to remove non-malicious anomalies, specifically in collaborative filtering systems. A review of the state-of-the-art in this field shows that no previous work has been carried out for recommendation systems and general data mining scenarios, to exactly perform this preprocessing task. More specifically, in this paper we propose a method that is based on the extraction of knowledge from the dataset in the form of rating regularities (similar to frequent patterns), and their use in order to remove anomalous preferences provided by users. Experiments show that the application of the procedure as a preprocessing step improves the performance of a data-mining task associated with the recommendation and also effectively detects the anomalous preferences.

• Journal of the Korean Society of Safety
• /
• v.37 no.3
• /
• pp.9-15
• /
• 2022

The analysis of the causes of accidents in workplaces where machines and tools are used is essential to improve the effectiveness and efficiency of safety prevention policies in places of employment in Korea. The causes of workplace accidents are not fully understood mainly due to difficulties in analyzing available descriptive information. This study focuses on the automated accident cause analysis in workplaces based on the accident abstracts found in industrial accident reports written in an unstructured descriptive format. The method proposed in this paper is based on text data mining and uses the keyword search function of Excel software to automate the analysis. The analysis results indicate that the primary reason for the frequency of accidents is related to technical aspects at a stage in which dangerous situations occur in the workplace. Accidents due to managerial causes are typically observed when danger exists in the workplace; however, managerial actions play a more important role in reducing accident severity. A small company tends to use unsafe machines and devices, leading to further accidents due to technical causes, whereas managerial causes are more conspicuous as the company grows. To preclude the occurrence of accidents due to inadequate knowledge, the implementation of safety management and the provision of safety education to elderly workers at the early stage of their employment are particularly important for small companies with less than 100 workers.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2018.07a
• /
• pp.59-62
• /
• 2018

XML을 대상으로 하는 연구가 최근 5~6년 사이에 꾸준한 증가를 보이며 이루어지고 있지만 대다수의 연구들은 XML을 구성하고 있는 엘리먼트 자체에 대한 통계적인 모델을 기반으로 이루어졌다. 이는 XML의 고유 속성인 트리 구조에서의 텍스트, 문장, 문장 구성 성분이 가지고 있는 의미(semantics)가 명시적으로 분석, 표현되어 사용되기 보다는 통계적인 방법으로만 데이터의 발생을 계산하여 사용자가 요구한 질의에 대한 결과, 즉 해당하는 정보 및 지식을 제공하는 형식이다. 지능형 지식서비스 제공을 위한 환경에 부합하기 위한 정보 추출은, 텍스트 및 문장의 구성 요소를 분석하여 문서의 내용을 단순한 단어 집합보다는 풍부한 의미를 내포하는 형식으로 표현함으로써 보다 정교한 지식과 정보의 추출이 수행될 수 있도록 하여야 한다. 본 연구는 범람하는 XML 데이터로부터 사용자 요구의 의미까지 파악하여 정확하고 다양한 지식을 추출할 수 있는 방법을 연구하고자 한다. 레코드 구조가 아닌 트리 구조 데이터로부터 의미 추출이 가능한 효율적인 마이닝 기법을 진일보시킴으로써 다양한 사용자 중심의 서비스 제공을 최종 목적으로 한다.

• Journal of Multimedia Information System
• /
• v.2 no.2
• /
• pp.233-240
• /
• 2015

Mobile devices are becoming increasingly sophisticated and the latest generation of smartphones now incorporates many diverse and powerful sensors. These sensors include acceleration sensor, magnetic field sensor, light sensor, proximity sensor, gyroscope sensor, pressure sensor, rotation vector sensor, gravity sensor and orientation sensor. The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper, we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity that a user is performing. To implement our system, we collected labeled accelerometer data from 10 users as they performed daily activities such as "phone detached", "idle", "walking", "running", and "jumping", and then aggregated this time series data into examples that summarize the user activity 5-minute intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users-just by having them carry cell phones in their pockets.