Mining association rules from quantitative data☆

Hong, Tzung-Pei; Kuo, Chan-Sheng; Chi, Sheng-Chai

doi:10.3233/ida-1999-3504

Cited by 44 publications

(72 citation statements)

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“…Finally, this method obtains the fuzzy association rules by the criterion used in the apriori algorithm (Agrawal and Srikant 1994). Hong et al (1999) proposed a mining approach that integrated fuzzy-sets concepts with the apriori algorithm to find interesting itemsets and fuzzy association rules in the instances with quantitative values. Although this approach could quickly find interesting patterns, some patterns might be missed since only the linguistic term with the maximum cardinality in each item is used in the mining process.…”

Section: Fuzzy Data-mining Algorithm For Quantitative Valuesmentioning

confidence: 99%

Mining fuzzy association rules from low-quality data

Gacto

Alcalá‐Fdez

2011

Soft Comput

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Data mining is most commonly used in attempts to induce association rules from databases which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Different studies have proposed methods for mining association rules from databases with crisp values. However, the data in many real-world applications have a certain degree of imprecision. In this paper we address this problem, and propose a new data-mining algorithm for extracting interesting knowledge from databases with imprecise data. The proposed algorithm integrates imprecise data concepts and the fuzzy apriori mining algorithm to find interesting fuzzy association rules in given databases. Experiments for diagnosing dyslexia in early childhood were made to verify the performance of the proposed algorithm.

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Section: Fuzzy Data-mining Algorithm For Quantitative Valuesmentioning

confidence: 99%

Mining fuzzy association rules from low-quality data

Gacto

Alcalá‐Fdez

2011

Soft Comput

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“…Since the item milk has three possible linguistic terms, Low, Middle and High, the membership functions for milk are thus encoded as (5,5,10,5,15,5) according to Fig. 2.…”

Section: Chromosome Representationmentioning

confidence: 99%

“…As to fuzzy data mining, Hong et al [10] proposed an algorithm to mine fuzzy rules from quantitative data. They transformed each quantitative item into a fuzzy set and used fuzzy operations to find fuzzy rules.…”

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confidence: 99%

A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

et al. 2006

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Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. The fitness of each chromosome is evaluated by the number of large 1-itemsets generated from part of the previously proposed fuzzy mining algorithm and by the suitability of the membership functions. Experimental results also show the effectiveness of the framework.

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“…We can thus divide the fuzzy data mining approaches into two kinds, namely single-minimum-support fuzzy-mining (SSFM) and multiple-minimum-support fuzzy-mining (MSFM) problems. Several mining approaches (Chan and Au 1997;Hong et al 1999;Kuok et al 1998;Yue et al 2000) have been proposed for the SSFM problem. Chan and Au proposed an F-APACS algorithm to mine fuzzy association rules (Chan and Au 1997).…”

Section: Introductionmentioning

confidence: 99%

“…Kuok et al (1998) proposed a fuzzy mining approach to handle numerical data in databases and derived fuzzy association rules. At nearly the same time, Hong et al (1999) proposed a fuzzy mining algorithm to mine fuzzy rules from quantitative transaction data. Basically, these fuzzy mining algorithms first used membership functions to transform each quantitative value into a fuzzy set in linguistic terms and then used a fuzzy mining process to find fuzzy association rules.…”

Section: Introductionmentioning

confidence: 99%

A genetic-fuzzy mining approach for items with multiple minimum supports

et al. 2008

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Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Mining association rules from transaction data is most commonly seen among the mining techniques. Most of the previous mining approaches set a single minimum support threshold for all the items and identify the relationships among transactions using binary values. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions under a single minimum support. In real applications, different items may have different criteria to judge their importance. In this paper, we thus propose an algorithm which combines clustering, fuzzy and genetic concepts for extracting reasonable multiple minimum support values, membership functions and fuzzy association rules from quantitative transactions. It first uses the k-means clustering approach to gather similar items into groups. All items in the same cluster are considered to have similar characteristics and are assigned similar values for initializing a better population. Each chromosome is then evaluated by the criteria of requirement satisfaction and suitability of membership functions to estimate its fitness value. Experimental results also show the effectiveness and the efficiency of the proposed approach.

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Mining association rules from quantitative data☆

Cited by 44 publications

References 0 publications

Mining fuzzy association rules from low-quality data

Mining fuzzy association rules from low-quality data

A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

A genetic-fuzzy mining approach for items with multiple minimum supports

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