• Title, Summary, Keyword: Association rules

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Exploration of Association Rules for Social Survey Data

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • 한국데이터정보과학회:학술대회논문집
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    • pp.18-24
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    • 2005
  • The methods of data mining are decision tree, association rules, clustering, neural network and so on. Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. We analyze Gyeongnam social indicator survey data by 2003 using association rule technique for environment information. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial and retail sectors. We can use association rule outputs in environmental preservation and environmental improvement.

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A Measure for Improvement in Quality of Association Rules in the Item Response Dataset (문항 응답 데이터에서 문항간 연관규칙의 질적 향상을 위한 도구 개발)

  • Kwak, Eun-Young;Kim, Hyeoncheol
    • The Journal of Korean Association of Computer Education
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    • v.10 no.3
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    • pp.1-8
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    • 2007
  • In this paper, we introduce a new measure called surprisal that estimates the informativeness of transactional instances and attributes in the item response dataset and improve the quality of association rules. In order to this, we set artificial dataset and eliminate noisy and uninformative data using the surprisal first, and then generate association rules between items. And we compare the association rules from the dataset after surprisal-based pruning with support-based pruning and original dataset unpruned. Experimental result that the surprisal-based pruning improves quality of association rules in question item response datasets significantly.

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Encoding of XML Elements for Mining Association Rules

  • Hu Gongzhu;Liu Yan;Huang Qiong
    • The Journal of Information Systems
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    • v.14 no.3
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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.

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A Study on the Development of Causal Knowledge Base Based on Data Mining and Fuzzy Cognitive Map (데이터 마이닝과 퍼지인식도 기반의 인과관계 지식베이스 구축에 관한 연구)

  • Kim, Jin-Sung
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.247-250
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    • 2003
  • Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

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A Critical Look at the Prague Rules: Rules on the Efficient Conduct of Proceedings in International Arbitration

  • Jun, Jung Won
    • Journal of Arbitration Studies
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    • v.29 no.3
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    • pp.53-74
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    • 2019
  • Due to the increasingly popular dissatisfaction regarding the inefficiency of arbitral proceedings, the Rules on the Efficient Conduct of Proceedings in International Arbitration, also known as the Prague Rules, was launched in December 2018, with the purpose of increasing the efficiency of arbitral proceedings by encouraging arbitral tribunals to take a more proactive role in conducting their procedures. In this article, the provisions of the Prague Rules are examined, in light of those of the IBA Rules on the Taking of Evidence in International Arbitration, in order to determine the efficacy of the Prague Rules on enhancing the efficiency in arbitral proceedings. The author concludes that more specific and detailed provisions, with respect to what the Rules means by such a "proactive arbitral tribunal," should have been explicitly included in light of the Rules' repeated emphasis on such. Also, the prospective outlook on the Prague Rules is not entirely clear as the text does not appear to fill in the gaps in other widely utilized arbitration rules or to supplement them in a satisfying way. However, given that only a short amount of time has passed since the launch late last year, only time will reveal how effective the Prague Rules will be in increasing the efficiency of arbitral proceedings, in accordance with its intended effect.

Analyzing the Location Decision of the Large-Scale Discount Store Using the Spatial Association Rules Mining (공간 연관규칙을 이용한 대형할인점의 입지 분석)

  • Lee Yong-Ik;Hong Sung-Eon;Kim Jung-Yup;Park Soo-Hong
    • Journal of the Korean Geographical Society
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    • v.41 no.3
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    • pp.319-330
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    • 2006
  • The objective of this research is to achieve an objectivity of site decision after extracting site decision factors on a large-scale discount store(LSDS) and utilize any hidden information using the association rules mining through huge database. To catch this objective, we collect a census, economic, and environmental dataset related with locating of LSDS. And then, we construct a spatial data on the research area. These data is used for the extraction of a spatial association rules. To verify whether the extracted rules are suitability or not, we use the sales of some LSDS. As the result of test, the more sales, the more factors of the extracted rules relate with the sales it coincides. Consequently, the spatial association rules mining is efficient method which support the ideal site decision of LSDS.

Discovering Time Weighted Association Rules (시간 가중치를 고려한 연관규칙)

  • 손승현;김재련
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.23 no.61
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    • pp.51-58
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    • 2000
  • Discovery of association rules has been used useful in many fields, especially in the fields of the inventory display, catalog design and cross selling. In previous works, all transactions In the database are treated uniformly. In this paper, we present a method for partitioning transactions in the database using time weights. Transactions are assigned different weights as time goes on. Examples show that these method provides purchasing patterns in the database as well as finding association rules.

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Data-Driven Exploration for Transient Association Rules (한시적 연관규칙을 위한 데이타 주도 탐사 기법)

  • Cho, Ll-Rae;Kim, Jong-Deok;Lee, Do-Heon
    • The Transactions of the Korea Information Processing Society
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    • v.4 no.4
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    • pp.895-907
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    • 1997
  • The mining of assciation rules disovers the trndency of events ocuring simultaneously in large databases. Previous announced research on association rules deals with associations with associations with respect to the whole transaction. However, xome association rules could have very high confidence in a sub-range of the time domain, even though they do not have quite high confidence in the whole time domain. Such kind of association rules are ecpected to be very usdful in various decion making problems.In this paper, we define transient association rule, as an association with high cimfidence worthy of special attention in a partial time interval, and propose an dfficeint algorithm wich finds out the time intervals appropriate to transient association rules from large-databases.We propose the data-driven retrival method excluding unecessary interval search, and design an effective data structure manageable in main memory obtined by one scanning of database, which offers the necessary information to next retrieval phase. In addition, our simulation shows that the suggested algorithm has reliable performance at the time cost acceptable in application areas.

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A Time-based Apriori Algorithm (아이템 사용시간을 고려한 Apriori알고리즘)

  • Kang, Hyung-Chang;Yang, Kun-Tak;Kim, Chul-Soo;Rhee, Yoon-Jung;Lee, Bong-Kyu
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.59 no.7
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    • pp.1327-1331
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    • 2010
  • Association rules are very useful and interesting patterns for discovering preferences of each person in digital-content services. The Apriori algorithm is an influential algorithm for mining frequent itemsets for association rules. However, since this algorithm does not take into account reference times of each content as an important support factor, it cannot be used to extract associations among time-based data. This paper proposes an augmented Apriori algorithm discovers association rules using both frequencies and usage times of each item.

Mining Association Rules on Significant Rare Data using Relative Support (상대 지지도를 이용한 의미 있는 희소 항목에 대한 연관 규칙 탐사 기법)

  • Ha, Dan-Shim;Hwang, Bu-Hyun
    • Journal of KIISE:Databases
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    • v.28 no.4
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    • pp.577-586
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    • 2001
  • Recently data mining, which is analyzing the stored data and discovering potential knowledge and information in large database is a key research topic in database research data In this paper, we study methods of discovering association rules which are one of data mining techniques. And we propose a technique of discovering association rules using the relative support to consider significant rare data which have the high relative support among some data. And we compare and evaluate existing methods and the proposed method of discovering association rules for discovering significant rare data.

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