Generalized Fuzzy C-Means Clustering with Improved Fuzzy Partitions and Shadowed Sets

Zabihi, Seyed Mohsen; Akbarzadeh-T, Mohammad-R.

doi:10.5402/2012/929085

Cited by 12 publications

(4 citation statements)

References 17 publications

(19 reference statements)

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“…The corpus has 30,000 annotated sentences that are not classified (about 49,8000 words). Therefore, a fuzzy c‐means clustering algorithm (Zabihi & Akbarzadeh, ) was performed in order to make the corpus applicable for test on the ontology. About 73 different clusters were found, of which four clusters were selected (peaks of clusters by number of documents), but the number of classified documents in each cluster still were not sufficient.…”

Section: Resultsmentioning

confidence: 99%

FNLP‐ONT: A feasible ontology for improving NLP tasks in Persian

2018

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Natural language processing is a composition of several error‐prone and challenging tasks, including part of speech tagging, word sense disambiguation, named entity recognition, and compound verb detection. Studying intrasentence relations and roles is essential to improve the mentioned subtasks. Semi‐automatic schemes such as ontologies can be applied to clarify word's dependencies. This paper presents an ontology that is targeting to improve POS tagging, WSD, NER, and compound verb detection in Persian with extra properties that may ameliorate machine translation. The ontology is tested in combinations with several state‐of‐art algorithms on Dadegan corpus. The results show that coping semantic analysis with machine learning methods enhance relation detection and consequently precision of the mentioned subtasks, which is not widely addressed in Persian. Furthermore, the experimental results declare that the accuracy rate increases between 4.5 and 23% for different tasks.

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Section: Resultsmentioning

confidence: 99%

FNLP‐ONT: A feasible ontology for improving NLP tasks in Persian

2018

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“…Several fuzzy clustering algorithms employ distance criteria including FCM, which uses reverse distance for fuzzy membership. In this case, feature vectors are part of all clusters with a zero to one coefficient (Webb 2003;Zabihi and Akbarzadeh-T 2012) .…”

Section: Fuzzy C-means (Fcm) Clusteringmentioning

confidence: 99%

Machine learning predictive approaches for Cu-Au mineral prospectivity map in theSonajil, NW of Iran: An improvement by Bayesian semi-supervised algorithm

Mohammadjafar

Majid

Jahangiri

et al. 2023

Preprint

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Most machine learning monitored algorithms used in the preparation of mineral potential prediction maps, require noise-free data to achieve high-performance and reliable results. To discover the hidden structures of a data set, unsupervised clustering methods are highly efficient. Therefore, in this paper, a combination of supervised and unsupervised methods was attempted using training and testing data to prepare a potential map with high accuracy in the Sonajil copper-gold deposit, located in NW of Iran. Here a semi-supervised Bayesian algorithm utilized to map the mineral landscape. At First, raster layers of 10 exploratory features were prepared. Then, 27 exploratory boreholes drilled were divided into 4 classes C1 to C4 based on the copper concentration, and from each class, two boreholes were selected and 100-meter buffering was performed around these boreholes to extract 1113 training data. Then, the existing data were clustered by the FCM method and the whole data and clustering data were entered into the Bayesian algorithm to evaluate the accuracy of Bayesian classifier method in 5 different clusters. The results show an increase in the average accuracy while using the clustered data instead of whole data for MPM mapping. Also, in case of not using cluster 5 data, it has the highest accuracy of 96%. To validate the Bayesian semi-supervised method, boreholes that were not used in data training were employed. Overall results based on the classification of boreholes and prepared potential map indicate accuracy of the Bayesian semi-supervised method as an optimal result for further exploration and delimiting more mineralization targets.

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“…Because of the constraints in ( 1 ), all points must completely allocate their memberships to each cluster [ 19 ]. The fuzzy weight centre of gravity of the data is used to define the cluster centre (centroid)X.…”

Section: The Proposed Mr-lsdgm Techniquementioning

confidence: 99%

LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications

Harshavardhan

Boyapati²,

Neelakandan

et al. 2022

Journal of Healthcare Engineering

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Several studies aimed at improving healthcare management have shown that the importance of healthcare has grown in recent years. In the healthcare industry, effective decision-making requires multicriteria group decision-making. Simultaneously, big data analytics could be used to help with disease detection and healthcare delivery. Only a few previous studies on large-scale group decision-making (LSDGM) in the big data-driven healthcare Industry 4.0 have focused on this topic. The goal of this work is to improve healthcare management decision-making by developing a new MapReduce-based LSDGM model (MR-LSDGM) for the healthcare Industry 4.0 context. Clustering decision-makers (DM), modelling DM preferences, and classification are the three stages of the MR-LSDGM technique. Furthermore, the DMs are subdivided using a novel biogeography-based optimization (BBO) technique combined with fuzzy C-means (FCM). The subgroup preferences are then modelled using the two-tuple fuzzy linguistic representation (2TFLR) technique. The final classification method also includes a feature extractor based on long short-term memory (LSTM) and a classifier based on an ideal extreme learning machine (ELM). MapReduce is a data management platform used to handle massive amounts of data. A thorough set of experimental analyses is carried out, and the results are analysed using a variety of metrics.

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Generalized Fuzzy C-Means Clustering with Improved Fuzzy Partitions and Shadowed Sets

Cited by 12 publications

References 17 publications

FNLP‐ONT: A feasible ontology for improving NLP tasks in Persian

FNLP‐ONT: A feasible ontology for improving NLP tasks in Persian

Machine learning predictive approaches for Cu-Au mineral prospectivity map in theSonajil, NW of Iran: An improvement by Bayesian semi-supervised algorithm

LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications

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