• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.231-242
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• 2007

The existing pattern mining methods[1,2,3,4,5,6,11,12,13] do not use location generalization method on the set of location history data of moving object, but even so they simply do extract only frequent patterns which have no spatio-temporal constraint in moving patterns on specific space. Therefore, it is difficult for those methods to apply to frequent pattern mining which has spatio-temporal constraint such as optimal moving or scheduling paths among the specific points. And also, those methods are required more large memory space due to using pattern tree on memory for reducing repeated scan database. Therefore, more effective pattern mining technique is required for solving these problems. In this paper, in order to develop more effective pattern mining technique, we propose new location generalization method that converts data of detailed level into meaningful spatial information for reducing the processing time for pattern mining of a massive history data set of moving object and space saving. The proposed method can lead the efficient spatial moving pattern mining of moving object using by creating moving sequences through generalizing the location attributes of moving object into 2D spatial area based on $R^*$-Tree and Area Grid Hash Table(AGHT) in preprocessing stage of pattern mining.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.30-36
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• 2001

We present a new algorithm for mining association rules in the large database. Association rules are the relationships of items in the same transaction. These rules provide useful information for marketing. Since Apriori algorithm was introduced in 1994, many researchers have worked to improve Apriori algorithm. However, the drawback of Apriori-based algorithm is that it scans the transaction database repeatedly. The algorithm which we propose scans the database twice. The first scanning of the database collects frequent length l-itemsets. And then, the algorithm scans the database one more time to construct the data structure Common-Item Tree which stores the information about frequent itemsets. To find all frequent itemsets, the algorithm scans Common-Item Tree instead of the database. As scanning Common-Item Tree takes less time than scanning the database, the algorithm proposed is more efficient than Apriori-based algorithm.

• 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.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.372-380
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• 2010

Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cuts costs, or both. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Data mining is one of the fastest growing field in the computer industry. Because of According to computer technology has been improving, Massive customer data has stored in database. Using this massive data, decision maker can extract the useful information to make a valuable plan with data mining. Data mining offers service providers great opportunities to get closer to customer. Data mining doesn't always require the latest technology, but it does require a magic eye that looks beyond the obvious to find and use the hidden knowledge to drive marketing strategies. Automotive market face an explosion of data arising from customer but a rate of increasing customer is getting lower. therefore, we need to determine which customer are profitable clients whom you wish to hold. This paper builds model of customer loyalty detection and analyzes customer buying patterns in automotive market with data mining using decision tree as a quinlan C4.5 and basic statics methods.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.49-63
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• 2009

The performance of spatio-temporal moving pattern mining depends on how to analyze and process the huge set of spatio-temporal data due to the nature of it. The several method was presented in order to solve the problems in which existing spatio-temporal moving pattern mining methods[1-10] have, such as increasing execution time and required memory size during the pattern mining, but they did not solve properly yet. Thus, we proposed the STMP/MST method[11] as a preceding research in order to extract effectively sequential and/or periodical frequent occurrence moving patterns from the huge set of spatio-temporal moving data. The proposed method reduces patterns mining execution time, using the moving sequence tree based on hash tree. And also, to minimize the required memory space, it generalizes detailed historical data including spatio-temporal attributes into the real world scopes of space and time by using spatio-temporal concept hierarchy. In this paper, in order to verify the effectiveness of the STMP/MST method, we compared and analyzed performance with existing spatio-temporal moving pattern mining methods based on the quantity of mining data and minimum support factor.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.855-864
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• 2013

Decision tree classification is one of the important problems in data mining fields and data minings have been important tasks in the fields of large database technologies. Therefore the coupling efforts of data mining systems and database systems have led the developments of database primitives supporting data mining functions such as decision tree classification. These primitives consist of the special database operations which support the SQL implementation of decision tree classification algorithms. These primitives have become the consisting modules of database systems for the implementations of the specific algorithms. There are two aspects in the developments of database primitives which support the data mining functions. The first is the identification of database common primitives which support data mining functions by analysis. The other is the provision of the extended mechanism for the implementations of these primitives as an interface of database systems. In data mining, some primitives want be stored in DBMS is one of the difficult problems. In this paper, to solve of the problem, we describe the database primitives which construct and apply the optimized decision tree classifiers. Then we identify the useful operations for various classification algorithms and discuss the implementations of these primitives on the commercial DBMS. We implement these primitives on the commercial DBMS and present experimental results demonstrating the performance comparisons.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.3
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• pp.155-164
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• 2016

For frequency pattern analysis of large databases, the new tree-based frequency pattern analysis algorithm which can compensate for the disadvantages of the Apriori method has been variously studied. In frequency pattern tree, the number of nodes is associated with memory allocation, but also affects memory resource consumption and processing speed of the growth. Therefore, reducing the number of nodes in the tree is very important in the frequency pattern mining. However, the absolute criteria which need to order the transaction items for construction frequency pattern tree has lowered the compression ratio of the tree nodes. But most of the frequency based tree construction methods adapted the absolute criteria. FP-tree is typically frequency pattern tree structure which is an extended prefix-tree structure for storing compressed frequent crucial information about frequent patterns. For construction the tree, all the frequent items in different transactions are sorted according to the absolute criteria, frequency descending order. CanTree also need to absolute criteria, canonical order, to construct the tree. In this paper, we proposed a novel frequency pattern tree construction method that does not use the absolute criteria, IRFP-tree algorithm. IRFP-tree(Intersection Rule based FP-tree). IRFP-tree is constituted with the new paradigm of the intersection rule without the use of the absolute criteria. It increased the compression ratio of the tree nodes, and reduced the tree construction time. Our method has the additional advantage that it provides incremental mining. The reported test result demonstrate the applicability and effectiveness of the proposed approach.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.287-296
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• 2002

웹 사용 마이닝은 데이터마이닝을 바탕으로 사용자의 로그 파일 정보를 이용하여 웹이 이용되는 패턴을 발견한다. 이를 이용하여 웹을 개선하여 사용자들이 보다 빨리 원하는 내용을 검색할 수 있도록 할 수 있으며 시스템 관리자에게는 효율적인 웹 구조를 인한 정보를 제공할 수 있다. 웹 사용 마이닝에서 사용하는 데이터는 성형화되어 있지 않으며 웹 사용 패턴을 분석하는데 방해가 되는 잡음 데이터까지 포함하고 있다. 이것은 기존에 개발된 여러 데이터마이닝 기법을 적용하는데 어려움으로 작용한다. 이러한 어려움을 해결하기 위해 본 논문에서는 새로운 방법을 도입한 SPMiner을 .제안한다. SPMiner는 웹의 구조를 이용하여 로그 파일의 전처리 과정을 줄이며 사용자의 탐색 패턴 분석을 효율적으로 수행 할 수 있는 시스템이다. SPMiner는 WebTree 에이전트를 이용하여 웹 사이트 구조를 분석하여 WebTree를 생성하고 사용자 로그 파일을 분석하여 각 웹 페이지의 사용빈도에 대한 정보를 추출한다. WebTree와 로그 파일에서 추출된 웹 페이지에 대한 정보는 SPMiner에 의해 패턴을 분석할 퍼 이용될 수 있는 형태인 WebTree$^{+}$로 병합된다 WebTree$^{+}$는 패턴 발견을 쉽게 해주며 사용자에게 추천할 정보나 웹 페이지를 능동적으로 추천할 수 있게 만들어 준다.

• Annual Conference of KIPS
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• 2008.11a
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• pp.275-278
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• 2008

Recently, there are many research of datamining. On the transaction dataset, association rules is made by finding of interesting patterns. A part of mining, sub-structure mining is increased in interest of and applied to many high technology. But graph mining has more computing time then itemset mining. Therefore, that need efficient way for avoid duplication. GASTON is best algorithm of duplication free. This paper analyze GASTON and expect the future work.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.23-30
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• 2015

As BigData is issued, many applications that operate based on the results of data analysis have been developed, typically applications are products recommend service of e-commerce application service system, search service on the search engine service and friend list recommend system of social network service. In this paper, we suggests a decision frequent pattern tree that is combined the origin frequent pattern tree that is mining similar pattern to appear in the data set of the existing data mining techniques and decision tree based on the theory of computer science. The decision frequent pattern tree algorithm improves about problem of frequent pattern tree that have to make some a lot's pattern so it is to hard to analyze about data. We also proposes to model for a Mapredue framework that is a programming model to help to operate in distributed environment.