• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.1-11
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• 2006

Data mining technique is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. It has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on off-line or on-line and so on. We analyze Gyeongnam social indicator survey data by 2001 using twostep clustering technique for environment information. The twostep clustering is classified as a partitional clustering method. We can apply these twostep clustering outputs to environmental preservation and improvement.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.221-232
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• 2021

In recent years, with the existence of huge amounts of data stored in huge databases, the need for developing accurate tools for analyzing data and extracting information and knowledge from the huge and multi-source databases have been increased. Hence, new and modern techniques have emerged that will contribute to the development of all other sciences. Knowledge discovery techniques are among these technologies, one popular technique of knowledge discovery techniques is data mining which aims to knowledge discovery from huge amounts of data. Such modern technologies of knowledge discovery will contribute to the development of all other fields. Data mining is important, interesting technique, and has many different and varied algorithms; Therefore, this paper aims to present overview of data mining, and clarify the most important of those algorithms and their uses.

• 한국석유지질학회:학술대회논문집
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• spring
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• pp.15-21
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• 1998

Sparker profiles and sediment cores collected from the Korea Strait show a distinct pattern of stacked prograding wedges consisting of three distinct units. These wedges are interpreted as the lowstand deposits formed during glacioeustatic sea-level lowstands. Repeated sea-level falls during late Pleistocene with high sediment discharge from the paleo-Nakdong River system resulted in the formation of thick lowstand wedges.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.664-677
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• 2014

Wind energy has proven its viability by the emergence of countless wind turbines around the world which greatly contribute to the increased electrical generating capacity of wind farm operators. These infrastructures are usually deployed in not easily accessible areas; therefore, maintenance routines should be based on a well-guided decision so as to minimize cost. To aid operators prior to the maintenance process, a condition monitoring system should be able to accurately reflect the actual state of the wind turbine and its major components in order to execute specific preventive measures using as little resources as possible. In this paper, we propose a fault detection approach which combines cluster analysis and frequent pattern mining to accurately reflect the deteriorating condition of a wind turbine and to indicate the components that need attention. Using SCADA data, we extracted operational status patterns and developed a rule repository for monitoring wind turbine systems. Results show that the proposed scheme is able to detect the deteriorating condition of a wind turbine as well as to explicitly identify faulty components.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.473-476
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• 2003

Nowaday, Internet is doing the role of a large distributed information service tenter and various information and database servers managing it are in distributed network environment. However, the we have several difficulties in deciding the server to disposal input data depending on data properties. In this paper, we designed server mining mechanism and Intellectual data mining system architecture for the best efficiently dealing with input data pattern by using neural network among the various data in distributed environment. As a result, the new input data pattern could be operated after deciding the destination server according to dynamic binding method implemented by neural network. This mechanism can be applied Datawarehous, telecommunication and load pattern analysis, population census analysis and medical data analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.94-100
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• 2010

Data Mining is mainly used for pattern extracting and information discovery from collected data. However previous methods is difficult to reflect changing patterns with time. In this paper, we introduce Streaming Decision Tree(SDT) analyzing data with continuity, large scale, and changed patterns. SDT defines continuity data as blocks and extracts rules using a Decision Tree's learning method. The extracted rules are combined considering time of occurrence, frequency, and contradiction. In experiment, we applied time series data and confirmed resonable result.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3524-3531
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• 2010

In order to supply suitable services to users in ubiquitous computing environments, it is important to consider both location and time information which is related to all object and user's activity. To do this, in this paper, we design a spatial-temporal ontology considering user context and propose a system architecture for active mining user activity and service pattern. The proposed system is a framework for active mining user activity and service pattern by considering the relation between user context and object based on trigger system.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.716-718
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• 2004

The false alarm data in intrusion detection systems are divided into false positive and false negative. The false positive makes bad effects on the performance of intrusion detection system. And the false negative makes bad effects on the efficiency of intrusion detection system. Recently, the most of works have been studied the data mining technique for analysis of alert data. However, the false alarm data not only increase data volume but also change patterns of alert data along the time line. Therefore, we need a tool that can analyze patterns that change characteristics when we look for new patterns. In this paper, we focus on the false positives and present a framework for analysis of false alarm pattern from the alert data. In this work, we also apply incremental data mining techniques to analyze patterns of false alarms among alert data that are incremental over the time. Finally, we achieved flexibility by using dynamic support threshold, because the volume of alert data as well as included false alarms increases irregular.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.359-366
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• 2016

In order to provide useful services for user in ubiquitous environment, a technique that can get the helpful information considering user activity and preference is needed and also user's interest actually changes as time passes. Therefore, the discovering method which reflects the concern degree of service information is needed. In this paper, we present the finding method of frequent pattern with dynamic weight on individual item based on service ontology we design. Our method can be applied to provide interested service information for user depending on context.

• Journal of Digital Contents Society
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• v.15 no.5
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• pp.569-577
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• 2014

The various services are required to user in time and space. It is important to provide suitable service to user according to user's circumstance. Therefore it is need to provide services to user through mining by latest information of user activity and service history. In this paper we propose a mining method to search association rule using service history based on spatiotemporal information and service ontology. In this method, we find the associative service pattern using level-cross tree on service ontology. The proposed method is to be a basic research to find the service pattern to provide high quality service to user according to season, location and age under the same context.