• 한국정보시스템학회지:정보시스템연구
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• 제14권3호
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• pp.37-47
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• 2005

Mining of association rules is to find associations among data items that appear together in some transactions or business activities. As of today, algorithms for association rule mining, as well as for other data mining tasks, are mostly applied to relational databases. As XML being adopted as the universal format for data storage and exchange, mining associations from XML data becomes an area of attention for researchers and developers. The challenge is that the semi-structured data format in XML is not directly suitable for traditional data mining algorithms and tools. In this paper we present an encoding method to encode XML tree-nodes. This method is used to store the XML data in Value Table and Transaction Table that can be easily accessed via indexing. The hierarchical relationship in the original XML tree structure is embedded in the encoding. We applied this method to association rules mining of XML data that may have missing data.

• 대한기계학회논문집A
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• 제39권12호
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• pp.1213-1220
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• 2015

망간단괴를 채집하는 심해저 통합 채광시스템은 채광선, 수직양광관, 중간버퍼, 유연관, 채광로봇으로 구성되어 있다. 최근 심해저 통합 채광시스템 연구에서는 생산성을 극대화하기 위한 다수의 채광로봇으로 구성되는 새로운 다중로봇 통합 채광시스템의 개념이 소개되었다. 본 논문에서는 다중로봇 통합 채광시스템 해석의 효율성을 향상시키고, 다중로봇 시스템의 확장이 이하도록 부분시스템 합성방법이 적되었다. 또한 유연 다물체 동역학이 적된 부분구조로 나눔으로써 수직양광관과 유연관의 대변위가 고려되었다. 일반적인 해석방법과 부분시스템 합성방법의 산술 연산 횟수를 비교함으로써 통합 채광시스템의 부분시스템 합성방법의 이론적인 효율성 연구가 수행되었다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권12호
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• pp.623-634
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• 2016

현존하는 빈발 패턴 마이닝 방법은 대부분 시간 효율성을 목표로 하고, 물리적 메모리 사용에 매우 의존적이다. 하지만 빅데이터 시대가 도래함에 따라 실제 세상의 데이터베이스는 급속도로 증가하고 있으며, 그에 따라 기존의 방법으로 현실적인 거대한 양의 데이터를 마이닝하기에 물리적 메모리 공간이 부족한 실정이다. 이러한 문제를 해결하기 위해, 빈발 패턴 마이닝의 메모리 의존성을 줄이기 위한 보조저장장치 기반의 연구들이 진행되었으나, 메모리 기반의 방법들에 비해 처리 시간이 너무 많이 소비된다는 한계가 있었다. 따라서 확장성을 가지며, 기존의 디스크 기반의 방법들에 비해 시간효율성을 높인 새로운 빈발 패턴 마이닝이 필요하게 되었다. 본 논문에서는 빅데이터로부터 빈도 아이템 집합들을 마이닝하기 위해 메모리와 디스크를 함께 사용하는 스택 기반의 새로운 접근법인 PPFP 알고리즘을 제안하였다. PPFP는 빈발 패턴 마이닝 접근법 중 가장 인기 있고 효율적인 접근법 중 하나인 FP-growth를 기반으로 하고 있다. PPFP 마이닝 방법은 다음과 같이 두 단계로 진행된다. (1) IFP-tree 구축: FP-tree를 생성한 후, 새로운 인덱스 번호 부여 방법으로 FP-tree의 각 노드에 인덱스 번호를 부여하고, 이 인덱스 번호가 부여된 FP-tree(IFP-tree)를 테이블로 변환하여(IFP-table) 디스크에 저장한다. (2) PPFP 알고리즘을 이용한 빈발 패턴 마이닝: 스택 기반의 PUSH-POP 방식으로 패턴을 확장시켜 나가며 빈발 패턴을 마이닝한다. 이러한 방식을 통해 메모리 기반의 방법에 비해 반복적으로 많은 시간이 소모되는 연산에 매우 적은 양의 메모리를 활용하여 확장성과 함께 시간효율성 또한 향상시킬 수 있었다. 그리고 기존의 연구 방법들과 비교 실험을 통해 새로운 알고리즘의 성능을 증명하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.594-597
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• 2007

We present in this paper a novel mid and long term power load prediction method using temporal pattern mining from AMR (Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.698-701
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• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권3호
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• pp.210-214
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• 2002

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.627-630
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• 대한원격탐사학회지
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• 제22권5호
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• pp.457-462
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• 한국컴퓨터정보학회논문지
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• 제24권2호
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• pp.171-177
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• 2019

This paper aims to find the most effective feature selection method for the sake of opinion mining tasks. Basically, opinion mining tasks belong to sentiment analysis, which is to categorize opinions of the online texts into positive and negative from a text mining point of view. By using the five product groups dataset such as apparel, books, DVDs, electronics, and kitchen, TF-IDF and Bag-of-Words(BOW) fare calculated to form the product review feature sets. Next, we applied the feature selection methods to see which method reveals most robust results. The results show that the stacking classifier based on those features out of applying Information Gain feature selection method yields best result.

• Geomechanics and Engineering
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• 제20권2호
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• pp.113-120
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• 2020

Rock damage is the main cause of accidents in underground engineering. It is difficult to predict rock damage accurately by using only one parameter. In this study, a rock failure prediction model was established by using stress, energy, and damage. The prediction level was divided into three levels according to the ratio of the damage threshold stress to the peak stress. A classification predicting model was established, including the stress, energy, damage and AE impact rate using Bayesian method. Results show that the model is good practicability and effectiveness in predicting the degree of rock failure. On the basis of this, a multi-parameter classification predicting deterioration model of rock failure was established. The results provide a new idea for classifying and predicting rockburst.