Emphasizing the Digital Shift of Hospitality Towards Hyper-Personalization

2022 ◽  
pp. 1-19
Author(s):  
Nuno Gustavo ◽  
Elliot Mbunge ◽  
Miguel Belo ◽  
Stephen Gbenga Fashoto ◽  
João Miguel Pronto ◽  
...  
Keyword(s):  
Execution Time ◽  
Clustering Algorithms ◽  
Fuzzy C Means ◽  
The Past ◽  
Personalized Services ◽  
Fcm Clustering ◽  
Number Of Iterations

This chapter aims to review the tech evolution in hospitality, from services to eServices, that will provide hyper-personalization in the hospitality field. In the past, the services were provided by hotels through diligent staff and supported by standardized and weak technology that was not allowed to provide personalized services by itself. Therefore, the study applied K-means and FCM clustering algorithms to cluster online travelers' reviews from TripAdvisor. The study shows that K-means clustering outperforms fuzzy c-means in this study in terms of accuracy and execution time while fuzzy c-means converge faster than K-means clustering in terms of the number of iterations. K-means achieved 93.4% accuracy, and fuzzy c-means recorded 91.3% accuracy.

scholarly journals A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chunhua Ren ◽  
Linfu Sun
Keyword(s):  
Clustering Algorithms ◽  
Real Data ◽  
Local Information ◽  
Data Sets ◽  
Clustering Ensemble ◽  
K Nearest Neighbors ◽  
Fuzzy C Means ◽  
Clustering Quality ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

scholarly journals Cluster Head Selection and Packet Delivery Estimation Based on K-Means and FCM

2019 ◽  
Vol 8 (4) ◽  
pp. 10028-10036
Keyword(s):  
Comparative Study ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Time Frame ◽  
Cluster Head Selection ◽  
Delivery Time ◽  
Packet Delivery ◽  
Fuzzy C Means ◽  
Fcm Clustering

In this paper comparative study have been presented for the efficient cluster head selection based on k-means and fuzzy c-means (FCM) clustering algorithms. It is observed that the nodes assignment after the clustering is different through k-means and FCM. It is because of the variant initialization mechanism of the k-means and FCM. But the assignment of cluster does not affect the results. It is clearly depicted from the packet delivery time results by our approach. It shows that the k-means and FCM have the capability of CHs selection in the required time frame and it shows the effectiveness in different iterations also. When aggregate packet delivery has been considered the same situation has been observed which depicts the capability of our approach. K-means found to be faster in comparison to FCM.

Clustering Algorithm in Possibilistic Exponential Fuzzy C-Mean Segmenting Medical Images

2017 ◽  
Vol 30 ◽  
pp. 12-23 ◽  
Author(s):  
Chiranji Lal Chowdhary ◽  
D.P. Acharjya
Keyword(s):  
Clustering Algorithm ◽  
Medical Images ◽  
Clustering Algorithms ◽  
Probabilistic Constraints ◽  
Fuzzy C Means ◽  
Average Accuracy ◽  
Segmentation Approach ◽  
Fcm Clustering ◽  
Fuzzy Segmentation ◽  
Result Analysis

Different fuzzy segmentation methods were used in medical imaging from last two decades for obtaining better accuracy in various approaches like detecting tumours etc. Well-known fuzzy segmentations like fuzzy c-means (FCM) assign data to every cluster but that is not realistic in few circumstances. Our paper proposes a novel possibilistic exponential fuzzy c-means (PEFCM) clustering algorithm for segmenting medical images. This new clustering algorithm technology can maintain the advantages of a possibilistic fuzzy c-means (PFCM) and exponential fuzzy c-mean (EFCM) clustering algorithms to maximize benefits and reduce noise/outlier influences. In our proposed hybrid possibilistic exponential fuzzy c-mean segmentation approach, exponential FCM intention functions are recalculated and that select data into the clusters. Traditional FCM clustering process cannot handle noise and outliers so we require being added in clusters due to the reasons of common probabilistic constraints which give the total of membership’s degree in every cluster to be 1. We revise possibilistic exponential fuzzy clustering (PEFCM) which hybridize possibilistic method over exponential fuzzy c-mean segmentation and this proposed idea partition the data filters noisy data or detects them as outliers. Our result analysis by PEFCM segmentation attains an average accuracy of 97.4% compared with existing algorithms. It was concluded that the possibilistic exponential fuzzy c-means segmentation algorithm endorsed for additional efficient for accurate detection of breast tumours to assist for the early detection.

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

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Seyed Mohsen Zabihi ◽  
Mohammad-R Akbarzadeh-T
Keyword(s):  
Distance Measure ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Fuzzy Partitions ◽  
Shadowed Sets

Clustering involves grouping data points together according to some measure of similarity. Clustering is one of the most significant unsupervised learning problems and do not need any labeled data. There are many clustering algorithms, among which fuzzy c-means (FCM) is one of the most popular approaches. FCM has an objective function based on Euclidean distance. Some improved versions of FCM with rather different objective functions are proposed in recent years. Generalized Improved fuzzy partitions FCM (GIFP-FCM) is one of them, which uses norm distance measure and competitive learning and outperforms the previous algorithms in this field. In this paper, we present a novel FCM clustering method with improved fuzzy partitions that utilizes shadowed sets and try to improve GIFP-FCM in noisy data sets. It enhances the efficiency of GIFP-FCM and improves the clustering results by correctly eliminating most outliers during steps of clustering. We name the novel fuzzy clustering method shadowed set-based GIFP-FCM (SGIFP-FCM). Several experiments on vessel segmentation in retinal images of DRIVE database illustrate the efficiency of the proposed method.

Construct Knowledge Structure of Linear Algebra

2011 ◽  
Vol 211-212 ◽  
pp. 793-797
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih ◽  
Sue Fen Huang
Keyword(s):  
Knowledge Management ◽  
Linear Algebra ◽  
Fuzzy Clustering ◽  
Mahalanobis Distance ◽  
Clustering Algorithms ◽  
Knowledge Structure ◽  
Interpretive Structural Modeling ◽  
Cognitive Characteristics ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Apply interpretive structural modeling to construct knowledge structure of linear algebra. New fuzzy clustering algorithms improved fuzzy c-means algorithm based on Mahalanobis distance has better performance than fuzzy c-means algorithm. Each cluster of data can easily describe features of knowledge structures individually. The results show that there are six clusters and each cluster has its own cognitive characteristics. The methodology can improve knowledge management in classroom more feasible.

Management of Abstract Algebra Concepts Based on Knowledge Structure

2013 ◽  
Vol 284-287 ◽  
pp. 3537-3542
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih
Keyword(s):  
Mahalanobis Distance ◽  
Clustering Algorithm ◽  
Fuzzy Theory ◽  
Clustering Algorithms ◽  
Knowledge Structures ◽  
Interpretive Structural Modeling ◽  
Fuzzy C Means ◽  
Integrated Method ◽  
Combined Algorithm ◽  
Fuzzy C Means Algorithm

Knowledge Management of Mathematics Concepts was essential in educational environment. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. Fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. A new improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance (FCM-NM) is proposed. Use the best performance of clustering Algorithm FCM-NM in data analysis and interpretation. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of Mathematics Concepts to construct the model of features in the pattern recognition completely. This procedure will also useful for cognition diagnosis. To sum up, this integrated algorithm could improve the assessment methodology of cognition diagnosis and manage the knowledge structures of Mathematics Concepts easily.

scholarly journals Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2344 ◽  
Author(s):  
Enwen Li ◽  
Linong Wang ◽  
Bin Song ◽  
Siliang Jian
Keyword(s):  
Fault Diagnosis ◽  
Membership Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Fuzzy C Means ◽  
Dissolved Gas Analysis ◽  
Fcm Clustering ◽  
Transformer Fault

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

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