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Hong, Tzung-Pei; Lin, Kuei-Ying; Chien, Been-Chian

doi:10.1023/a:1020991105855

Cited by 50 publications

(9 citation statements)

References 22 publications

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“…Similar to original apriori, the large itemsets need to be found firstly. Then, the mining process based on fuzzy counts is employed to find fuzzy association rules [33,34]. The procedure for fuzzy apriori based setpoint association rule mining is summarized in Algorithm I.…”

Section: Association Rule Miningmentioning

confidence: 99%

Fuzzy Association Rule Based Froth Surface Behavior Control in Zinc Froth Flotation

Zhang

Tang

et al. 2018

Symmetry

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Froth flotation is a vital mineral concentration process. Froth surface behavior is the knowledge about flotation working condition. However, in computer vision aided froth surface behavior control, there are still two challenges that need to be tackled seriously. Against the difficulty in the froth surface behavior representation, this paper proposes to combine the bubble size distribution (BSD) and froth velocity distribution. As far as we know, this is the first time that the froth velocity distribution is presented. Against the difficulty in the adaptive generation of the optimal froth surface behavior feature (optimal setpoint), this paper introduces the fuzzy apriori to mine the association rule between the current working condition and the optimal setpoint. Then, a fuzzy inference module is constructed to generate optimal setpoint for current working condition adaptively. Many validation experiments and comparison experiments demonstrate the superiority and robustness of the proposed methods.

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Section: Association Rule Miningmentioning

confidence: 99%

Fuzzy Association Rule Based Froth Surface Behavior Control in Zinc Froth Flotation

Zhang

Tang

et al. 2018

Symmetry

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“…In Figure 1, the root node is at level 0, the internal nodes representing categories (such as "milk") are at level 1, the internal nodes representing flavors (such as "chocolate") are at level 2, and the terminal nodes representing brands (such as "Foremost") are at level 3. Only terminal nodes appear in transactions [3]. In predefined taxonomies are first encoded using sequences of numbers and the symbol "*" according to their positions in the hierarchy tree.…”

Section: Multilevel Association Conceptmentioning

confidence: 99%

“…In predefined taxonomies are first encoded using sequences of numbers and the symbol "*" according to their positions in the hierarchy tree. For example, the internal node "Milk" in Figure 1 would be represented by 1**, the internal node "Chocolate" by 11*, and the terminal node "Dairyland" by 111 [3].…”

Section: Multilevel Association Conceptmentioning

confidence: 99%

“…In this part we present our algorithm which is called improved fuzzy mining algorithm using taxonomy (IFMAT) which is modified the version of previous fuzzy mining algorithm presented in [3]. It consist of data mining, multi-level taxonomy and a set of membership functions to explore fuzzy association rules in accordance a given transaction dataset.…”

Section: The Proposed Modelmentioning

confidence: 99%

“…The mining approaches, found from the classes where the information is issued, may be classified as finding association rules, classification rules, clustering rules and sequential rules and etc. An Association rule mining is an important process in data mining, which determines the correlation between items belonging to a transaction database [1][2][3]. Association rules are extensively carried out and are useful for planning and marketing.…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Mining Fuzzy Multiple-Level Association Rules from Quantitative Data

Kousari¹,

Mirabedini²,

Ghasemkhani³

2012

JSEA

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Data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level. Extracting multilevel association rules in transaction databases is most commonly used in data mining. This paper proposes a multilevel fuzzy association rule mining model for extraction of implicit knowledge which stored as quantitative values in transactions. For this reason it uses different support value at each level as well as different membership function for each item. By integrating fuzzy-set concepts, data-mining technologies and multiple-level taxonomy, our method finds fuzzy association rules from transaction data sets. This approach adopts a top-down progressively deepening approach to derive large itemsets and also incorporates fuzzy boundaries instead of sharp boundary intervals. Comparing our method with previous ones in simulation shows that the proposed method maintains higher precision, the mined rules are closer to reality, and it gives ability to mine association rules at different levels based on the user’s tendency as well

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Fuzzy frameworks for mining data associations: fuzzy association rules and beyond

Marín

Ruiz

Sánchez

2016

WIREs Data Min & Knowl

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Looking for associations in data is one of the data mining tasks that has aroused more interest in the literature. In this area, incorporating concepts of fuzzy set theory is useful in problems where imprecision and/or uncertainty appear. In most of the existing approaches, fuzzy association rules are widely seen as fuzzy rules, which are very different in nature from association rules, so problems like fuzzy inclusion and cardinality have been seldom taken into account explicitly, mostly providing ad hoc solutions for capturing semantics. In contrast, in this study we have taken the more general and natural approach of considering the elements of the association rule mining framework and studying possible and sensible fuzzy extensions, referred here as fuzzy frameworks for mining associations. As fuzzy frameworks are abstract mathematical models, another key contribution of the article is the notion of interpretations as mappings between fuzzy frameworks and specific datasets. This general analysis of the field is completed with a study of various important aspects that arise when proposing quality measures in a fuzzy environment, as well as those related to computational issues. The work also includes a review of the fuzzy framework that arises from fuzzy transactions regarded as fuzzy subsets of items, and shows that many of the approaches on fuzzy association rules that exist in the literature can be placed in the context of a proper interpretation of that framework. WIREs Data Mining Knowl Discov 2016, 6:50–69. doi: 10.1002/widm.1176 This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Association Rules

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Untitled

Cited by 50 publications

References 22 publications

Fuzzy Association Rule Based Froth Surface Behavior Control in Zinc Froth Flotation

Fuzzy Association Rule Based Froth Surface Behavior Control in Zinc Froth Flotation

Improvement of Mining Fuzzy Multiple-Level Association Rules from Quantitative Data

Fuzzy frameworks for mining data associations: fuzzy association rules and beyond

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