• Title/Summary/Keyword: 빈발 패턴

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An associative service mining based on dynamic weight (동적 가중치 기반의 연관 서비스 탐사 기법)

  • Hwang, Jeong Hee
    • 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.

Mining Frequent Contiguous Sequence Patterns in Biological Sequences (생물학적 서열들에서 빈발한 연속 서열 패턴 마이닝)

  • Kang, Tae-Ho;Yoo, Jae-Soo
    • Proceedings of the Korean Information Science Society Conference
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    • 2007.06b
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    • pp.27-31
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    • 2007
  • 생물학적 서열 데이터는 크게 DNA 염기 서열과 단백질 아미노산 서열이 있다. 이들 서열은 일반적으로 많은 수의 항목들을 가지고 있어 그 길이가 매우 길다. 생물학적 데이터 서열들에는 보통 빈번하게 발생하는 부분 연속 서열들이 존재하는데 이들 서열들을 찾아내는 것은 다양한 서열 분석에서 유용하게 사용될 수 있다. 이를 위해 초기에는 Apriori 알고리즘을 기반으로 하는 순차패턴 마이닝 알고리즘들을 활용하는 방법들이 많이 제시되었다. 그중 PrefixSpan 알고리즘은 Apriori기반의 가장 효율적인 순차패턴 마이닝 기법이다. 하지만 이 알고리즘은 길이-1인 빈발 패턴들로부터 서열 패턴을 확장해나가는 방식으로 길이가 긴 연속 서열을 포함하는 생물학적 데이터 서열들에 대한 검색방법으로는 적합하지 않다. 최근에는 기존의 PrefixSpan방식을 이용하면서도 반복적인 처리과정을 줄인 MacosVSpan이 제안되었다. 하지만 이 알고리즘 또한 원본 데이터베이스보다 크기가 큰 별도의 프로젝션 데이터베이스를 사용함으로서 많은 비용부담이 발생하고 특히 길이가 긴 서열에 대해서는 더욱 효율적이지 못하다. 이에 본 논문에서 많은 양의 생물학적 데이터 서열들로부터 빈번한 연속서열을 고정길이 확장 트리를 이용하여 효과적으로 찾아내는 방법을 제안한다. 그리고 다양한 환경에서 실험을 통해 제안하는 방식이 MacosVSpan알고리즘에 비해 검색 성능이 우수함을 증명한다.

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The Efficient Spatio-Temporal Moving Pattern Mining using Moving Sequence Tree (이동 시퀀스 트리를 이용한 효율적인 시공간 이동 패턴 탐사 기법)

  • Lee, Yon-Sik;Ko, Hyun
    • The KIPS Transactions:PartD
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    • v.16D no.2
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    • pp.237-248
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    • 2009
  • Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.

A Method for Optimal Moving Pattern Mining using Frequency of Moving Sequence (이동 시퀀스의 빈발도를 이용한 최적 이동 패턴 탐사 기법)

  • Lee, Yon-Sik;Ko, Hyun
    • The KIPS Transactions:PartD
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    • v.16D no.1
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    • pp.113-122
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    • 2009
  • Since the traditional pattern mining methods only probe unspecified moving patterns that seem to satisfy users' requests among diverse patterns within the limited scopes of time and space, they are not applicable to problems involving the mining of optimal moving patterns, which contain complex time and space constraints, such as 1) searching the optimal path between two specific points, and 2) scheduling a path within the specified time. Therefore, in this paper, we illustrate some problems on mining the optimal moving patterns with complex time and space constraints from a vast set of historical data of numerous moving objects, and suggest a new moving pattern mining method that can be used to search patterns of an optimal moving path as a location-based service. The proposed method, which determines the optimal path(most frequently used path) using pattern frequency retrieved from historical data of moving objects between two specific points, can efficiently carry out pattern mining tasks using by space generalization at the minimum level on the moving object's location attribute in consideration of topological relationship between the object's location and spatial scope. Testing the efficiency of this algorithm was done by comparing the operation processing time with Dijkstra algorithm and $A^*$ algorithm which are generally used for searching the optimal path. As a result, although there were some differences according to heuristic weight on $A^*$ algorithm, it showed that the proposed method is more efficient than the other methods mentioned.

Finding Frequent Itemsets Over Data Streams in Confined Memory Space (한정된 메모리 공간에서 데이터 스트림의 빈발항목 최적화 방법)

  • Kim, Min-Jung;Shin, Se-Jung;Lee, Won-Suk
    • The KIPS Transactions:PartD
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    • v.15D no.6
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    • pp.741-754
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    • 2008
  • Due to the characteristics of a data stream, it is very important to confine the memory usage of a data mining process regardless of the amount of information generated in the data stream. For this purpose, this paper proposes the Prime pattern tree(PPT) for finding frequent itemsets over data streams with using the confined memory space. Unlike a prefix tree, a node of a PPT can maintain the information necessary to estimate the current supports of several itemsets together. The length of items in a prime pattern can be reduced the total number of nodes and controlled by split_delta $S_{\delta}$. The size and the accuracy of the PPT is determined by $S_{\delta}$. The accuracy is better as the value of $S_{\delta}$ is smaller since the value of $S_{\delta}$ is large, many itemsets are estimated their frequencies. So it is important to consider trade-off between the size of a PPT and the accuracy of the mining result. Based on this characteristic, the size and the accuracy of the PPT can be flexibly controlled by merging or splitting nodes in a mining process. For finding all frequent itemsets over the data stream, this paper proposes a PPT to replace the role of a prefix tree in the estDec method which was proposed as a previous work. It is efficient to optimize the memory usage for finding frequent itemsets over a data stream in confined memory space. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

Frequent Items Mining based on Regression Model in Data Streams (스트림 데이터에서 회귀분석에 기반한 빈발항목 예측)

  • Lee, Uk-Hyun
    • The Journal of the Korea Contents Association
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    • v.9 no.1
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    • pp.147-158
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    • 2009
  • Recently, the data model in stream data environment has massive, continuous, and infinity properties. However the stream data processing like query process or data analysis is conducted using a limited capacity of disk or memory. In these environment, the traditional frequent pattern discovery on transaction database can be performed because it is difficult to manage the information continuously whether a continuous stream data is the frequent item or not. In this paper, we propose the method which we are able to predict the frequent items using the regression model on continuous stream data environment. We can use as a prediction model on indefinite items by constructing the regression model on stream data. We will show that the proposed method is able to be efficiently used on stream data environment through a variety of experiments.

An Open Map API based-Prototype Utilizing Frequent Pattern Mining Technique for Efficient Service of Customized Land Information (맞춤형 국토정보의 효과적 제공을 위한 빈발 패턴 탐사 기법을 활용한 오픈맵 API 기반 프로토타입)

  • Lee, Dong-Gyu;Yi, Gyeong-Min;Shin, Dong-Mun;Kim, Jae-Chul;Ryu, Keun-Ho
    • Journal of Korea Spatial Information System Society
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    • v.12 no.1
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    • pp.95-99
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    • 2010
  • Spatial information systems have developed in order to provide users with customized land information in u-City environments. The spatial information systems can detect spatial information for users anytime anywhere. Information which is analyzed by data mining techniques can be offered for other users. Therefore, we propose open map API-based prototype which utilizes frequent pattern mining technique. Proposed prototype can mine interesting trip routes and unknown attractions in location data of geophoto. Also, proposed prototype is the first attempt which analyzes spatial patterns can be represented on a map which is selected by users. Our prototype can be applied to the smart phone like mobile devices.

Development and Application of An Adaptive Web Site Construction Algorithm (적응형 웹 사이트 구축을 위한 연관규칙 알고리즘 개발과 적용)

  • Choi, Yun-Hee;Jun, Woo-Chun
    • The KIPS Transactions:PartD
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    • v.16D no.3
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    • pp.423-432
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    • 2009
  • Advances in information and communication technologies are changing our society greatly. In knowledge-based society, information can be obtained easily via communication tools such as web and e-mail. However, obtaining right and up-to-date information is difficult in spite of overflowing information. The concept of adaptive web site has been initiated recently. The purpose of the site is to provide information only users want out of tons of data gathered. In this paper, an algorithm is developed for adaptive web site construction. The proposed algorithm is based on association rules that are major principle in adaptive web site construction. The algorithm is constructed by analysing log data in web server and extracting meaning documents through finding behavior patterns of users. The proposed algorithm has the following characteristics. First, it is superior to existing algorithms using association rules in time complexity. Its superiority is proved theoretically. Second, the proposed algorithm is effective in space complexity. This is due to that it does not need any intermediate products except a linked list that is essential for finding frequent item sets.

In-memory Compression Scheme Based on Incremental Frequent Patterns for Graph Streams (그래프 스트림 처리를 위한 점진적 빈발 패턴 기반 인-메모리 압축 기법)

  • Lee, Hyeon-Byeong;Shin, Bo-Kyoung;Bok, Kyoung-Soo;Yoo, Jae-Soo
    • The Journal of the Korea Contents Association
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    • v.22 no.1
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    • pp.35-46
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    • 2022
  • Recently, with the development of network technologies, as IoT and social network service applications have been actively used, a lot of graph stream data is being generated. In this paper, we propose a graph compression scheme that considers the stream graph environment by applying graph mining to the existing compression technique, which has been focused on compression rate and runtime. In this paper, we proposed Incremental frequent pattern based compression technique for graph streams. Since the proposed scheme keeps only the latest reference patterns, it increases the storage utilization and improves the query processing time. In order to show the superiority of the proposed scheme, various performance evaluations are performed in terms of compression rate and processing time compared to the existing method. The proposed scheme is faster than existing similar scheme when the number of duplicated data is large.

A Pattern Retrieval Method of Frequent Moving Objects Using Vertical-Based Framework (수직구조 기반의 빈발 이동 객체 패턴 탐색 기법)

  • Hong, Sung-Han;Hwang, Byung-Yeon
    • 한국공간정보시스템학회:학술대회논문집
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    • 2005.11a
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    • pp.75-79
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    • 2005
  • 최근에 모바일 기기의 급속한 발전과 더불어 다양한 서비스들이 개발되고 있다. 그 중에서도 위치 기반 서비스는 사용자에게 위치와 관련된 유용한 정보를 제공하는 서비스를 말한다. 효과적인 서비스를 제공하기 위해서는 먼저 위치정보를 나타내는 이동 객체 관련기술 연구가 선행되어야 한다. 이러한 연구의 핵심 기술로 현재 빈발한 이동 객체 탐사를 위한 마이닝 기법들에 관한 연구가 진행되고 있다. 본 연구에서는 기존의 수평적 마이닝 기법에서 문제시되었던 많은 후보 이동 객체 발생을 줄이기 위해 새로운 수직적 마이닝 기법을 적용한 방법을 제안한다.

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