Mining fuzzy specific rare itemsets for education data

Weng, Cheng-Hsiung

doi:10.1016/j.knosys.2011.02.010

Cited by 34 publications

(27 citation statements)

References 21 publications

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“…However, most of the association rule mining methods did not consider the ambiguity of data in the process of data pre-processing stage that cannot reflect the representative of data and human's cognition completely and truly (Kuok et al, 1998;Kaya & Alhajj, 2003;Weng, 2011;Matthews et al, 2013;Wang et al, 2015). Therefore, converting the data into linguistic data by using fuzzy sets has become an important research direction for the data mining application (Ashish & Vikramkumar, 2010;Weng, 2011;Sowan et al, 2013;Jin et al, 2014;Arafah & Mukhlash, 2015;Palacios et al, 2015;Khatib et al, 2015).…”

Section: Contribution Of This Paper To the Literaturementioning

confidence: 99%

“…A fuzzy set can be defined mathematically by assigning to each possible element in the universe of discourse a value representing its grade of membership in the fuzzy set (Zadeh, 1965;Weng, 2011;Sowan et al, 2013;Khatib et al, 2015;Palacios et al, 2015). A fuzzy number ̃ is a fuzzy set whose membership function ̃( ) satisfies the following conditions (Kaufmann, & Gupta, 1991;Klir and Yuan, 1995;:…”

Section: Fuzzy Set Theorymentioning

confidence: 99%

“…Data mining method can also apply to explore the data to describe the status of learners (Hu et al, 2014). Association rules is one of the most famous data mining methods, which explored the relationships among the data attributes (Agrawal et al, 1993;Han et al, 2001;Chen & Weng, 2009;Weng, 2011;Sowan et al, 2013;Shabana & Samuel, 2015;Palacios et al, 2015). An association rule mining is to generate all frequent itemsets and association rules that satisfy the minimum support and confidence values from the database.…”

Section: Introductionmentioning

confidence: 99%

“…The rare behaviors patterns are sometimes helpful to teachers to identify the learning problems of students. Therefore, rare behaviors mining is an important issue for the educational research (Weng, 2011;Hoque et al, 2012;Tsang et al, 2013;Troiano & Scibelli, 2014;Bhatt & Patel, 2015a;Bhatt & Patel, 2015b;Goyal et State of the literature  In the educational field, the most learning behaviors of students are normal and easy to find out the pattern from the educational database.  In fact, it needs more assistance for students who exhibit the negative behaviors.…”

Section: Introductionmentioning

confidence: 99%

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A Study on the Rare Factors Exploration of Learning Effectiveness by Using Fuzzy Data Mining

Chen

Chang

2017

EURASIA J MATH SCI T

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The phenomenon of low fertility has been negative impacted on the social structure of the educational environment in Taiwan. To increase the learning effectiveness of students became the most important issue for the Universities in Taiwan. Due to the subjective judgment of evaluators and the attributes of influenced factors are always fuzzy, it is not easy to measure the learning effectiveness by using the crisp values for the qualitative factors. In additional, the negative behaviors or the infrequent learning data (rare item sets) cannot easily excavate from educational database. Therefore, a systematic mining method is proposed here by combining fuzzy set theory with data mining technology to explore the key factors of learning effectiveness from the infrequent data. A case study implemented in this paper and showed that the proposed method can effectively find the important factors and valuable patterns from the survey data. The results of this paper can provide the important information for teachers to help students increase the learning effectiveness. Finally, the conclusions are addressed at the end of this paper.

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Section: Contribution Of This Paper To the Literaturementioning

confidence: 99%

Section: Fuzzy Set Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Study on the Rare Factors Exploration of Learning Effectiveness by Using Fuzzy Data Mining

Chen

Chang

2017

EURASIA J MATH SCI T

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“…Lee and Lee (2004) used fuzzy sets to describe calendar expressions of temporal patterns. Weng (2011) investigated the temporal property of not discovering rare fuzzy itemsets, which is different to our research, because we focus on how the fuzzy sets are defined instead of only the temporal property. Matthews et al (2011b;2011a) focused on the composition of fuzzy and temporal association rule mining, which is extended in this paper.…”

Section: Sg Matthews Et Almentioning

confidence: 99%

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Matthews¹,

Góngora²,

Hopgood³

2013

International Journal of Applied Mathematics and Computer Science

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A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method's ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

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