Efficient Web Log Mining Using Enhanced Apriori Algorithm with Hash Tree and Fuzzy

Veeramalai, S.; Jaisankar, N.; Kannan, A.; Chennai, Chennai

doi:10.5121/ijcsit.2010.2406

Cited by 11 publications

(1 citation statement)

References 16 publications

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“…Therefore, this paper applies the fuzzy set theory to the Apriori algorithm and uses it for mining the key state quantities. The Apriori algorithm based on fuzzy set theory can be found in …”

Section: Mining Of Key State Quantities Inducing Distribution Transfomentioning

confidence: 99%

A transfer learning fault diagnosis model of distribution transformer considering multi‐factor situation evolution

Yang¹,

Shen²,

Zhou³

et al. 2019

IEEJ Transactions Elec Engng

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Aiming at the problem of limited fault data and data expiration of distribution transformers, a transfer learning fault diagnosis model of the distribution transformer considering multi-factor situation evolution is proposed in this paper. First, the state quantities that influence the distribution transformer operation are constructed, and fuzzy binary quantification is used for the state quantities. The association between the state quantities and the fault is explored by the fuzzy Apriori algorithm, and the key state variables that induce the transformer fault are extracted. The Tanimoto coefficient is introduced for the limited fault data of distribution transformers, and the effective auxiliary fault data is transferred to the target distribution transformer. On this basis, the fault diagnosis model based on information transfer is proposed. A health index is introduced to describe the distribution health levels, and the auxiliary fault data in different health levels is transferred because data has expired. On this basis, the fault diagnosis model for data expiration is proposed. The weights of the target and auxiliary fault data in the above model are iteratively solved by using the TrAdaBoost algorithm. Finally, an example analysis is carried on the basis of distribution transformer fault data, and the simulation results show that the fault diagnosis accuracy of the model is high and that it has stronger generalization ability than traditional diagnosis models.

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Section: Mining Of Key State Quantities Inducing Distribution Transfomentioning

confidence: 99%