• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.152-155
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• 2010

실시간 데이터 마이닝 기법은 다양한 종류의 센서에서 수집된 다차원 스트림 데이터들 사이에 존재하는 의미있는 정보를 탐사할 수 있다. 전통적인 데이터베이스 시스템에서의 마이닝 기법은 정적인 데이터베이스에 기초하므로 실시간으로 수집되는 스트림 데이터는 시간 속성을 갖는 인터벌 이벤트로 요약되어야 한다. 이 논문은 다차원 스트림 데이터 환경에서 스트림 데이터를 요약하고 이들 사이에 존재하는 인과 관계를 탐사하는 실시간 데이터 마이닝 기법을 제안한다. 제안 기법은 센서에서 수집되는 데이터의 대부분이 객체의 정상적인 상태 데이터임을 고려하여 의미있는 이상 이벤트를 선별하여 전송한다. 그리고 스트림 데이터의 연속성을 고려하며 스트림 데이터를 세 가지 상태의 이벤트로 요약하고 인과 관계 규칙을 탐사한다. 인과 관계 규칙은 시간에 따라 이벤트 발생에 영향력을 미치는 원인 이벤트를 발견함으로써 이벤트의 발생을 미리 예측할 수 있다.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.673-682
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• 2009

XML data are widely used for data representation and exchange on the Web and the data type is an continuous stream in ubiquitous environment. Therefore there are some mining researches related to the extracting of frequent structures and the efficient query processing of XML stream data. In this paper, we propose a mining method to extract frequent structures of XML stream data in recent window based on the sliding window. XML stream data are modeled as a tree set, called XFP_tree and we quickly extract the frequent structures over recent XML data in the XFP_tree.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.28-30
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• 2003

컴퓨팅 환경의 발달로 방대한 양의 정보들이 매우 빠른 속도로 생성되고 있다. 구성 요소가 지속적으로 발생되는 무한 집합으로 정의되는 데이터 스트림에 대한 마이닝 방법은 이들 정보로부터 중요한 지식을 효과적으로 얻을 수 있는 방법으로 최근 들어 다양한 방법들이 활발히 제안되고 있다. 이러한 마이닝 방법에서는 지속적으로 확장되는 데이터 스트림의 특성으로 수행과정에서 메모리 사용량을 가용 범위 내로 제한하는 것이 중요한 고려 사항이 되고 있다. 본 논문에서는 데이터 스트림에서 빈발 항목을 탐색하는데 있어서 가용 메모리 범위에서 최적의 메모리를 사용하여 최상의 마이닝 결과를 얻을 수 있도록 하는 메모리 사용량 최적화 방법을 제시한다.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.335-344
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• 2005

Recently open data mining methods focusing on a data stream that is a massive unbounded sequence of data elements continuously generated at a rapid rate are proposed actively. Knowledge embedded in a data stream is likely to be changed over time. Therefore, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. This paper proposes a sliding window technique for finding recently frequent itemsets, which is applied efficiently in open data mining. In the proposed technique, its memory usage is kept in a small space by delayed-insertion and pruning operations, and its mining result can be found in a short time since the data elements within its target range are not traversed repeatedly. Moreover, the proposed technique focused in the recent data elements, so that it can catch out the recent change of the data stream.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.3
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• pp.383-393
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• 2013

Data stream mining is a technique to obtain the useful information by analyzing the data generated in real time. In data stream mining technology, frequent itemset mining is a method to find the frequent itemset while data is transmitting, and these itemsets are used for the purpose of pattern analyze and marketing in various fields. Existing techniques of finding frequent itemset mining are having problems when a malicious attacker sniffing the data, it reveals data provider's real-time information. These problems can be solved by using a method of inserting dummy data. By using this method, a attacker cannot distinguish the original data from the transmitting data. In this paper, we propose a method for privacy preserving frequent itemset mining by using the technique of inserting dummy data. In addition, the proposed method is effective in terms of calculation because it does not require encryption technology or other mathematical operations.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.34-37
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• 2007

간 및 낮은 메모리 사용량을 요구한다. 이러한 데이터 스트림에서의 데이터 마이닝은 전체 데이터에 대한 분석 보다는 사용자가 관심을 갖는 영역에 대한 마이닝에 초점이 맞추어져 있어, 사용자 관심영역에 대한 분석 데이터 탐색을 필요로 한다. 이에 본 논문에서는 기존의 분석 데이터 탐색 기법인 빙산 질의 및 상위-k 질의에 대하여 알아보고, 이를 보완하기 위한 확률에 기반한 데이터 탐색법인 확률기반 빙산 질의를 제안한다.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.1-9
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• 2015

In most techniques of information differentiating for data stream mining, they give larger weight to the information generated in recent compared to the old information. However, there can be important one among the old information. For example, in case of a person was a regular customer in a retail store but has not come to the store in recent, old information with the shopping record of the person can be importantly used in a target marketing for increasing sales. In this paper, highly attention itemsets(HAI) are defined, which mean the itemsets generated in the past frequently but not generated in recent. In addition, a twao-way decay mechanism and a data stream mining method for finding HAI are proposed.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.447-458
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• 2003

The basic assumption of conventional data mining methodology is that the data set of a knowledge discovery process should be fixed and available before the process can proceed. Consequently, this assumption is valid only when the static knowledge embedded in a specific data set is the target of data mining. In addition, a conventional data mining method requires considerable computing time to produce the result of mining from a large data set. Due to these reasons, it is almost impossible to apply the mining method to a realtime analysis task in a data stream where a new transaction is continuously generated and the up-to-dated result of data mining including the newly generated transaction is needed as quickly as possible. In this paper, a new mining concept, open data mining in a data stream, is proposed for this purpose. In open data mining, whenever each transaction is newly generated, the updated mining result of whole transactions including the newly generated transactions is obtained instantly. In order to implement this mechanism efficiently, it is necessary to incorporate the delayed-insertion of newly identified information in recent transactions as well as the pruning of insignificant information in the mining result of past transactions. The proposed algorithm is analyzed through a series of experiments in order to identify the various characteristics of the proposed algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.4183-4191
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• 2011

Considering the characteristics of a data stream whose data elements are continuously generated and may change over time, there have been many techniques to differentiate the importance of data elements in a data stream by their generation time. The conventional techniques are efficient to get an analysis result focusing on the recent information in a data stream, but they have a limitation to differentiate the importance of information in various ways more flexible. An information differentiation technique based on the term of a fuzzy set can be an alternative way to compensate the limitation. A term of a fuzzy set has been widely used in various data mining fields, which can overcome the sharp boundary problem and give an analysis result reflecting the requirements in real world applications more. In this paper, a fuzzy window mechanism is proposed, which is adapting a term of a fuzzy set and is efficiently used to differentiate the importance of information in mining data streams. Basic concepts including fuzzy calendars are described first, and subsequently details on data stream mining of weighted patterns using a fuzzy window technique are described.