• Title/Summary/Keyword: apriori algorithm

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A Study on Association-Rules for Recurrent Items Mining of Multimedia Data (멀티미디어 데이타의 재발생 항목 마이닝을 위한 연관규칙 연구)

  • 김진옥;황대준
    • Journal of Korea Multimedia Society
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    • v.5 no.3
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    • pp.281-289
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    • 2002
  • Few studies have been systematically pursued on a multimedia data mining in despite of the over-whelming amounts of multimedia data by the development of computer capacity, storage technology and Internet. Based on the preliminary image processing and content-based image retrieval technology, this paper presents the methods for discovering association rules from recurrent items with spatial relationships in huge data repositories. Furthermore, multimedia mining algorithm is proposed to find implicit association rules among objects of which content-based descriptors such as color, texture, shape and etc. are recurrent and of which descriptors have spatial relationships. The algorithm with recurrent items in images shows high efficiency to find set of frequent items as compared to the Apriori algorithm. The multimedia association-rules algorithm is specially effective when the collection of images is homogeneous and it can be applied to many multimedia-related application fields.

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Early Detection of Lung Cancer Risk Using Data Mining

  • Ahmed, Kawsar;Abdullah-Al-Emran, Abdullah-Al-Emran;Jesmin, Tasnuba;Mukti, Roushney Fatima;Rahman, Md. Zamilur;Ahmed, Farzana
    • Asian Pacific Journal of Cancer Prevention
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    • v.14 no.1
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    • pp.595-598
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    • 2013
  • Background: Lung cancer is the leading cause of cancer death worldwide Therefore, identification of genetic as well as environmental factors is very important in developing novel methods of lung cancer prevention. However, this is a multi-layered problem. Therefore a lung cancer risk prediction system is here proposed which is easy, cost effective and time saving. Materials and Methods: Initially 400 cancer and non-cancer patients' data were collected from different diagnostic centres, pre-processed and clustered using a K-means clustering algorithm for identifying relevant and non-relevant data. Next significant frequent patterns are discovered using AprioriTid and a decision tree algorithm. Results: Finally using the significant pattern prediction tools for a lung cancer prediction system were developed. This lung cancer risk prediction system should prove helpful in detection of a person's predisposition for lung cancer. Conclusions: Most of people of Bangladesh do not even know they have lung cancer and the majority of cases are diagnosed at late stages when cure is impossible. Therefore early prediction of lung cancer should play a pivotal role in the diagnosis process and for an effective preventive strategy.

A Study on Data Association-Rules Mining of Content-Based Multimedia (내용 기반의 멀티미디어 데이터 연관규칙 마이닝에 대한 연구)

  • Kim, Jin-Ok;Hwang, Dae-Jun
    • The KIPS Transactions:PartD
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    • v.9D no.1
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    • pp.57-64
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    • 2002
  • Few studies have been systematically pursued on a multimedia data mining in despite of the overwhelming amounts of multimedia data by the development of computer capacity, storage technology and Internet. Based on the preliminary image processing and content-based image retrieval technology, this paper presents the methods for discovering association rules from recurrent items with spatial relationships in huge data repositories. Furthermore, multimedia mining algorithm is proposed to find implicit association rules among objects of which content-based descriptors such as color, texture, shape and etc. are recurrent and of which descriptors have spatial relationships. The algorithm with recurrent items in images shows high efficiency to find set of frequent items as compared to the Apriori algorithm. The multimedia association-rules algorithm is specially effective when the collection of images is homogeneous and it can be applied to many multimedia-related application fields.

Transaction Pattern Discrimination of Malicious Supply Chain using Tariff-Structured Big Data (관세 정형 빅데이터를 활용한 우범공급망 거래패턴 선별)

  • Kim, Seongchan;Song, Sa-Kwang;Cho, Minhee;Shin, Su-Hyun
    • The Journal of the Korea Contents Association
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    • v.21 no.2
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    • pp.121-129
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    • 2021
  • In this study, we try to minimize the tariff risk by constructing a hazardous cargo screening model by applying Association Rule Mining, one of the data mining techniques. For this, the risk level between supply chains is calculated using the Apriori Algorithm, which is an association analysis algorithm, using the big data of the import declaration form of the Korea Customs Service(KCS). We perform data preprocessing and association rule mining to generate a model to be used in screening the supply chain. In the preprocessing process, we extract the attributes required for rule generation from the import declaration data after the error removing process. Then, we generate the rules by using the extracted attributes as inputs to the Apriori algorithm. The generated association rule model is loaded in the KCS screening system. When the import declaration which should be checked is received, the screening system refers to the model and returns the confidence value based on the supply chain information on the import declaration data. The result will be used to determine whether to check the import case. The 5-fold cross-validation of 16.6% precision and 33.8% recall showed that import declaration data for 2 years and 6 months were divided into learning data and test data. This is a result that is about 3.4 times higher in precision and 1.5 times higher in recall than frequency-based methods. This confirms that the proposed method is an effective way to reduce tariff risks.

SME Bakery's Marketing Strategies Based on Apriori Algorithm (Apriori 알고리즘 기반의 중소 베이커리 기업의 대응 전략)

  • Kim, Do Hoon;Lee, Hyeon June;Lee, Bong Gyou
    • Journal of Convergence for Information Technology
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    • v.12 no.4
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    • pp.328-337
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    • 2022
  • The importance of online marketing is emerging due to the prevalence of COVID-19. In order to respond to the changing business environment, we have collected ten years of sales data of SME bakery company that have experienced a decrease in sales due to the COVID-19. As a result of the analysis, we found that switching from offline markets to omnichannel B2B and B2C markets and taking 'small quantity batch production' to 'mass production in a small variety can improve management. This study presented online and offline marketing strategies through data analysis of small and medium-sized bakery companies, which have relatively insufficient digital capabilities compared to large companies, and could be a guideline for many SMEs.

Design and implementation of data mining tool using PHP and WEKA (피에이치피와 웨카를 이용한 데이터마이닝 도구의 설계 및 구현)

  • You, Young-Jae;Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.2
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    • pp.425-433
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    • 2009
  • 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 need a data mining tool to explore a lot of information. There are many data mining tools or solutions; E-Miner, Clementine, WEKA, and R. Almost of them are were focused on diversity and general purpose, and they are not useful for laymen. In this paper we design and implement a web-based data mining tool using PHP and WEKA. This system is easy to interpret results and so general users are able to handle. We implement Apriori algorithm of association rule, K-means algorithm of cluster analysis, and J48 algorithm of decision tree.

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An Association Discovery Algorithm Containing Quantitative Attributes with Item Constraints (수량적 속성을 포함하는 항목 제약을 고려한 연관규칙 마이닝 앨고리듬)

  • 한경록;김재련
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.22 no.50
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    • pp.183-193
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    • 1999
  • The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In this paper, we propose an efficient algorithm for mining quantitative association rules with item constraints. For categorical attributes, we map the values of the attribute to a set of consecutive integers. For quantitative attributes, we can partition the attribute into values or ranges. While such constraints can be applied as a post-processing step, integrating them into the mining algorithm can reduce the execution time. We consider the problem of integrating constraints that are boolean expressions over the presence or absence of items containing quantitative attributes into the association discovery algorithm using Apriori concept.

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An Algorithm for Sequential Sampling Method in Data Mining (데이터 마이닝에서 샘플링 기법을 이용한 연속패턴 알고리듬)

  • 홍지명;김낙현;김성집
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.21 no.45
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    • pp.101-112
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    • 1998
  • Data mining, which is also referred to as knowledge discovery in database, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases. The discovered knowledge can be applied to information management, decision making, and many other applications. In this paper, a new data mining problem, discovering sequential patterns, is proposed which is to find all sequential patterns using sampling method. Recognizing that the quantity of database is growing exponentially and transaction database is frequently updated, sampling method is a fast algorithm reducing time and cost while extracting the trend of customer behavior. This method analyzes the fraction of database but can in general lead to results of a very high degree of accuracy. The relaxation factor, as well as the sample size, can be properly adjusted so as to improve the result accuracy while minimizing the corresponding execution time. The superiority of the proposed algorithm will be shown through analyzing accuracy and efficiency by comparing with Apriori All algorithm.

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Design and Implementation of e-SRM System Supporting Individual Adjusting Feedback in Web-based Learning Environment (웹 기반 학습 환경에서 개별 적응적 피드백을 지원하는 e-SRM 시스템의 설계 및 구현)

  • Baek, Jang-Hyeon;Kim, Yung-Sik
    • Journal of The Korean Association of Information Education
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    • v.8 no.3
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    • pp.307-317
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    • 2004
  • In web-based education environment, it is necessary to provide individually adjusting feedback according to learner's characteristic. Despite this necessity, it is a current state that there are difficulties in deriving the variables of learners' characteristics and lack in developing the systematic strategies and practical tools for providing individually adjusting feedback. This study analyzed the learners' learning patterns, one of learner's characteristic variables regarded as important in web-based teaching and learning environment by employing Apriori algorithm, and also grouped the learners by learning pattern. Under this framework, the e-SRM feedback system was designed and developed to provide learning content, learning channel, and learning situation, etc. for individual learners. The proposed system in this study is expected to provide an optimal learning environment complying with learner's characteristic.

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Dynamic Modeling and Adaptive Neural-Fuzzy Control for Nonholonomic Mobile Manipulators Moving on a Slope

  • Liu Yugang;Li Yangmin
    • International Journal of Control, Automation, and Systems
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    • v.4 no.2
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    • pp.197-203
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    • 2006
  • This paper addresses dynamic modeling and task-space trajectory following issues for nonholonomic mobile manipulators moving on a slope. An integrated dynamic modeling method is proposed considering nonholonomic constraints and interactive motions. An adaptive neural-fuzzy controller is presented for end-effector trajectory following, which does not rely on precise apriori knowledge of dynamic parameters and can suppress bounded external disturbances. Effectiveness of the proposed algorithm is verified through simulations.