• Title/Summary/Keyword: Quantitative Association Rules

Search Result 40, Processing Time 0.02 seconds

Generalized Fuzzy Quantitative Association Rules Mining with Fuzzy Generalization Hierarchies

  • Lee, Keon-Myung
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.2 no.3
    • /
    • pp.210-214
    • /
    • 2002
  • Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

Mining Quantitative Association Rules using Commercial Data Mining Tools (상용 데이타 마이닝 도구를 사용한 정량적 연관규칙 마이닝)

  • Kang, Gong-Mi;Moon, Yang-Sae;Choi, Hun-Young;Kim, Jin-Ho
    • Journal of KIISE:Databases
    • /
    • v.35 no.2
    • /
    • pp.97-111
    • /
    • 2008
  • Commercial data mining tools basically support binary attributes only in mining association rules, that is, they can mine binary association rules only. In general, however. transaction databases contain not only binary attributes but also quantitative attributes. Thus, in this paper we propose a systematic approach to mine quantitative association rules---association rules which contain quantitative attributes---using commercial mining tools. To achieve this goal, we first propose an overall working framework that mines quantitative association rules based on commercial mining tools. The proposed framework consists of two steps: 1) a pre-processing step which converts quantitative attributes into binary attributes and 2) a post-processing step which reconverts binary association rules into quantitative association rules. As the pre-processing step, we present the concept of domain partition, and based on the domain partition, we formally redefine the previous bipartition and multi-partition techniques, which are mean-based or median-based techniques for bipartition, and are equi-width or equi-depth techniques for multi-partition. These previous partition techniques, however, have the problem of not considering distribution characteristics of attribute values. To solve this problem, in this paper we propose an intuitive partition technique, named standard deviation minimization. In our standard deviation minimization, adjacent attributes are included in the same partition if the change of their standard deviations is small, but they are divided into different partitions if the change is large. We also propose the post-processing step that integrates binary association rules and reconverts them into the corresponding quantitative rules. Through extensive experiments, we argue that our framework works correctly, and we show that our standard deviation minimization is superior to other partition techniques. According to these results, we believe that our framework is practically applicable for naive users to mine quantitative association rules using commercial data mining tools.

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
    • /
    • v.22 no.50
    • /
    • pp.183-193
    • /
    • 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.

  • PDF

Discovery of Association Rules Base on Data of Time Series and Quantitative Attribute (시간적 관계와 수량적 가중치 따른 연관규칙 발견)

  • 양신모;정광호;김진수;이정현
    • Proceedings of the IEEK Conference
    • /
    • 2003.11b
    • /
    • pp.207-210
    • /
    • 2003
  • In this paper, we explore a new data mining capability that is based on Quantitative Attribute and Time Series. Our solution procedure consists of two steps. First, We derive an algorithm to contain the Quantitative Attribute into a set of candidate item. Second, We redefine the concepts of confidence and support for composite association rules. It is shown that proposed methode is very advantageous and can lead to prominent performance improvement.

  • PDF

Efficient Quantitative Association Rules with Parallel Processing (병렬처리를 이용한 효율적인 수량 연관규칙)

  • Lee, Hye-Jung;Hong, Min;Park, Doo-Soon
    • Journal of Korea Multimedia Society
    • /
    • v.10 no.8
    • /
    • pp.945-957
    • /
    • 2007
  • Quantitative association rules apply a binary association to the data which have the relatively strong quantitative attributions in a large database system. When a domain range of quantitative data which involve the significant meanings for the association is too broad, a domain requires to be divided into a proper interval which satisfies the minimum support for the generation of large interval items. The reliability of formulated rules is enormously influenced by the generation of large interval items. Therefore, this paper proposes a new method to efficiently generate the large interval items. The proposed method does not lose any meaningful intervals compared to other existing methods, provides the accurate large interval items which are close to the minimum support, and minimizes the loss of characteristics of data. In addition, since our method merges data where the frequency of data is high enough, it provides the fast run time compared with other methods for the broad quantitative domain. To verify the superiority of proposed method, the real national census data are used for the performance analysis and a Clunix HPC system is used for the parallel processing.

  • PDF

Performance Estimation of Fuzzr Quantitative Association Rules and Crisp Quantitative Association Rules (퍼지 연관규칙과 연관규칙의 성능 평가)

  • 손영경;김명원
    • Proceedings of the Korean Information Science Society Conference
    • /
    • 2002.04b
    • /
    • pp.235-237
    • /
    • 2002
  • 연관규칙(association rule)이란 데이터 베이스에 존재하는 속성들 사이에 유사성 또는 패턴을 기술하는 것으로, 사용자에게 데이터에 관한 유용한 조보를 줄 수 있다. 그러나, 지금가지의 연관규칙은 이진 (boolean) 데이터 베이스에 존재하는 연관규칙의 발견에 대해서 주로 연구되어 왔으며, 정량적(수치적, quantitative) 속성을 갖는 데이터에 대한 연관규칙의 연구는 미비하였다. 그 이유는 정량적 속성을 갖는 데이터를 기호적(nominal) 속성값으로 바꾼 후 연관규칙 보다 성능이 우수함을 보이고 있다. 또한 본 논문에서는 퍼지 연관규칙에서 소속함수(항목, 아이템, 속성값)의 모양과 개수를 데이터 분포에 대한 통계적 특성을 나타내는 히스토그램을 이용하여 소속함수를 자동 생성하는 효율적인 연관규칙 추출방법을 제안한다

  • PDF

Measuring the Confidence of Human Disaster Risk Case based on Text Mining (텍스트마이닝 기반의 인적재난사고사례 신뢰도 측정연구)

  • Lee, Young-Jai;Lee, Sung-Soo
    • The Journal of Information Systems
    • /
    • v.20 no.3
    • /
    • pp.63-79
    • /
    • 2011
  • Deducting the risk level of infrastructure and buildings based on past human disaster risk cases and implementing prevention measures are important activities for disaster prevention. The object of this study is to measure the confidence to proceed quantitative analysis of various disaster risk cases through text mining methodology. Indeed, by examining confidence calculation process and method, this study suggests also a basic quantitative framework. The framework to measure the confidence is composed into four stages. First step describes correlation by categorizing basic elements based on human disaster ontology. Secondly, terms and cases of Term-Document Matrix will be created and the frequency of certain cases and terms will be quantified, the correlation value will be added to the missing values. In the third stage, association rules will be created according to the basic elements of human disaster risk cases. Lastly, the confidence value of disaster risk cases will be measured through association rules. This kind of confidence value will become a key element when deciding a risk level of a new disaster risk, followed up by preventive measures. Through collection of human disaster risk cases related to road infrastructure, this study will demonstrate a case where the four steps of the quantitative framework and process had been actually used for verification.

Comparative Study of Quantitative Data Binning Methods in Association Rule

  • Choi, Jae-Ho;Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
    • /
    • v.19 no.3
    • /
    • pp.903-911
    • /
    • 2008
  • Association rule mining searches for interesting relationships among items in a given large database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. Many data is most quantitative data. There is a need for partitioning techniques to quantitative data. The partitioning process is referred to as binning. We introduce several binning methods ; parameter mean binning, equi-width binning, equi-depth binning, clustering-based binning. So we apply these binning methods to several distribution types of quantitative data and present the best binning method for association rule discovery.

  • PDF

Mining Generalized Fuzzy Quantitative Association Rules with Fuzzy Generalization Hierarchies (퍼지 일반화 계층을 이용한 일반화된 퍼지 정량 연관규칙 마이닝)

  • 한상훈;손봉기;이건명
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • 2001.05a
    • /
    • pp.8-11
    • /
    • 2001
  • 연관규칙 마이닝은 트랜잭션 데이터를 이루고 있는 항목간의 잠재적인 의존관계를 발견하는 데이터 마이닝의 한 분야이다. 정량 연관규칙이란 부류적 속성과 정량적 속성을 모두 포함한 연관규칙이다. 정량 연관규칙 마아닝을 위한 퍼지 기술의 응용, 정량 연관규칙 마이닝을 위한 일반화된 연관규칙 마이닝, 사용자의 관심도를 반영한 중요도 가중치가 있는 연관규칙 마이닝 등에 대한 연구가 이루어져 왔다. 이 논문에서는 중요도 가중치가 있는 일반화된 퍼지 정량 연관규칙 마이닝의 새로운 방법을 제안한다. 이 방법은 부류적 속성의 퍼지 개념 계층과 정량적 속성의 퍼지 언어항 일반화 계층을 일반화된 추출하기 위해 이용한다. 이것은 속성들의 수준별 일반화 계층과 속성의 중요도 가중치를 이용함으로써 사용자가 보다 융통성 있는 연관규칙을 마이닝할 수 있게 해준다.

  • PDF

Analysis on Relation between Rehabilitation Training Movement and Muscle Activation using Weighted Association Rule Discovery (가중연관규칙 탐사를 이용한 재활훈련운동과 근육 활성의 연관성 분석)

  • Lee, Ah-Reum;Piao, Youn-Jun;Kwon, Tae-Kyu;Kim, Jung-Ja
    • Journal of the Institute of Electronics Engineers of Korea CI
    • /
    • v.46 no.6
    • /
    • pp.7-17
    • /
    • 2009
  • The precise analysis of exercise data for designing an effective rehabilitation system is very important as a feedback for planing the next exercising step. Many subjective and reliable research outcomes that were obtained by analysis and evaluation for the human motor ability by various methods of biomechanical experiments have been introduced. Most of them include quantitative analysis based on basic statistical methods, which are not practical enough for application to real clinical problems. In this situation, data mining technology can be a promising approach for clinical decision support system by discovering meaningful hidden rules and patterns from large volume of data obtained from the problem domain. In this research, in order to find relational rules between posture training type and muscle activation pattern, we investigated an application of the WAR(Weishted Association Rule) to the biomechanical data obtained mainly for evaluation of postural control ability. The discovered rules can be used as a quantitative prior knowledge for expert's decision making for rehabilitation plan. The discovered rules can be used as a more qualitative and useful priori knowledge for the rehabilitation and clinical expert's decision-making, and as a index for planning an optimal rehabilitation exercise model for a patient.