Mining generalized positive and negative inter-cross fuzzy multiple-level coherent rules

Anuradha, R.; Rajkumar, N.

doi:10.3233/jifs-16240

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…In the practice of applying fuzzy sets, the extension of the membership function is quite important [44][45][46]. When it comes to practical research topics, the range of values of fuzzy membership could be extended.…”

Section: Membership Extension and Classification Of Hsimentioning

confidence: 99%

Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data

et al. 2018

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Over the years, with the efforts of many researchers, the field of species distribution model (SDM) has been well explored. The model of fuzzy matter elements (FME), which, combined with GIS to predict species distribution, has received extensive attention since its emergence. Based on previous studies, this paper improved FME, extended the scope of the membership degree and habitat suitability index, and explored the unsuitable areas of species. We have enhanced the limitation effect of key variables on species habitats, making the operation of FME more consistent with biological laws. By optimizing the FME, it could avoid the accumulation of predicted errors with multi-variables, and make the predicted results more reasonable. In this study, Gynostemma pentaphyllum (Thunb.) Makino was used as an example. The experimental process used several major environmental variables (climate, soil, and terrain variables) to predict the habitat suitability distribution of G. pentaphyllum in China for its current and future period, which includes the period of 2050s (average for 2041–2060) and 2070s (average for 2061–2080) under representative concentration pathways 4.5 (RCP4.5). The results of the analysis showed that the model performed well with a high accuracy by reducing the redundancy of the environmental data. The study could relieve the reliance on a large database of environmental information and propose a new approach for protecting the G. pentaphyllum in unsuitable areas under climate change.

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Section: Membership Extension and Classification Of Hsimentioning

confidence: 99%

Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data

et al. 2018

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“…(X → Y) is a negative association rule if the presence of X assures the absence of Y in the database. Many studies have been carried out to mine positive and negative association rules from different datasets [15][16][17][18]. Shaheen et al [7] introduced a variable called context that can essentially be used to mine valid positive and negative association rules.…”

Section: Introductionmentioning

confidence: 99%

CARM: Context Based Association Rule Mining for Conventional Data

Shaheen¹,

Abdullah²

2021

Computers, Materials &Amp; Continua

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This paper is aimed to develop an algorithm for extracting association rules, called Context-Based Association Rule Mining algorithm (CARM), which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm (CBPNARM). CBPNARM was developed to extract positive and negative association rules from Spatiotemporal (space-time) data only, while the proposed algorithm can be applied to both spatial and non-spatial data. The proposed algorithm is applied to the energy dataset to classify a country's energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative associations. Many association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning technique. The context, in this paper serves as a pruning measure to extract pertinent association rules from non-spatial data. Conditional Probability Increment Ratio (CPIR) is also added in the proposed algorithm that was not used in CBPNARM. The inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of use. Also, the extraction of a common negative frequent itemset in CARM is different from that of CBPNARM. The rules created by the proposed algorithm are more meaningful, significant, relevant and insightful. The accuracy of the proposed algorithm is compared with the Apriori, PNARM and CBPNARM algorithms. The results demonstrated enhanced accuracy, relevance and timeliness.

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“…Two methods of identifying the term hierarchy in an ontology have long been developed: one based on a rule template and the other based on statistics or machine learning (Anuradha & Rajkumar, 2017). The former is often combined with syntactic dependency analysis (Tsui, Wang, Cheung, & Lau, 2010); with this considered, language templates need to be developed manually, and the hierarchy available for large‐scale unstructured texts are relatively limited.…”

Section: Introductionmentioning

confidence: 99%

An ontology automation construction scheme for Chinese e‐government thesaurus optimizing

Wang

Zhang

Deng

et al. 2020

Proc Assoc Inf Sci Technol

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To optimize the term hierarchy in the manual e‐government thesaurus, we combine the mainstream knowledge organization technology to form a complete set of ontology automation construction scheme. We build an e‐government knowledge base by using subject words in the Comprehensive E‐government Thesaurus as the term set and encyclopedia text as the corpus. The specific work includes the extraction of semantic features from the bag‐of‐words model, determination of the number of clusters by linear and nonlinear dimensionality reduction, division of terms by spectral clustering, social network analysis to determine the class label, and storing knowledge ontology via OWL. The recall rate of term hierarchy in the ontology is excellent, indicating the ontology has good knowledge extensibility, and also proving the efficiency of the scheme proposed in this work. Besides, the application model of a term hierarchy in information retrieval can show a richer semantic relation than the original thesaurus to guide the retrieval extension of government information resources.

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Mining generalized positive and negative inter-cross fuzzy multiple-level coherent rules

Cited by 5 publications

References 21 publications

Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data

Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data

CARM: Context Based Association Rule Mining for Conventional Data

An ontology automation construction scheme for Chinese e‐government thesaurus optimizing

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