Using fuzzy clustering to reveal recurring spatial patterns in corpora of dialect maps

2012 ◽  
Vol 17 (2) ◽  
pp. 176-197 ◽  
Author(s):  
Daniel Meschenmoser ◽  
Simon Pröll
Keyword(s):  
Spatial Patterns ◽  
Fuzzy Clustering ◽  
Statistical Properties ◽  
New Method ◽  
Clustering Method ◽  
Semantic Relationships ◽  
Fuzzy C Means ◽  
Lexical Data ◽  
Empirical Covariance

In this article, a new method to identify groups of spatially similar dialect maps is presented. This is done by comparing statistical properties of the maps: the empirical covariance is measured for every map in a corpus of dialect maps. Then, the Fuzzy C-Means clustering method is applied to these covariance data. Thereby, one is able to detect and measure gradual similarities between maps. By employing the method on lexical data from the dialect atlas Sprachatlas von Bayerisch-Schwaben, it can be shown that clusters of spatially similar maps also share semantic similarities. This method can thus be used for grouping maps based on spatial similarities while at the same time indicating patterns of semantic relationships between spatially related variables.

scholarly journals Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 158
Author(s):  
Tran Dinh Khang ◽  
Nguyen Duc Vuong ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Clustering Method ◽  
Machine Learning Technique ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Learning Technique ◽  
Fuzzy C Means Clustering

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

Study on the Method for Fault Sample Selection Based on Fuzzy Clustering

2011 ◽  
Vol 219-220 ◽  
pp. 492-495 ◽  
Author(s):  
Hong Xia Wang ◽  
Shuang Shuang Liu ◽  
Xiao Hui Ye
Keyword(s):  
Fuzzy Clustering ◽  
Sample Selection ◽  
The Other ◽  
New Method ◽  
Clustering Method ◽  
Fuzzy Clustering Method ◽  
System Components ◽  
Detection Ratio ◽  
The Cost ◽  
Test Adequacy

A new method of fault sample selection is presented. First, the mapping relationship is established between the system components and system functions attributes according to the similarity of the system components and system functions attributes, the components are clustered to the class by fuzzy clustering method; then the fault sample is selected in the class according to relation of the faults propagation, the selected fault sample is validated by using the function adequacy and test adequacy. Finally From experimental results, the conclusion can be drown that the cost of this method is lower and the fault detection ratio is high. Compare with the other method the new method has certain advantages.

A Novel Fuzzy Clustering Method with No Outliers Influence

2013 ◽  
Vol 300-301 ◽  
pp. 735-739 ◽  
Author(s):  
Li Jen Kao ◽  
Yo Ping Huang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Human Robot Interaction ◽  
Hand Gesture ◽  
Clustering Method ◽  
Robot Interaction ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Fcm Clustering

Fuzzy C-Means (FCM) clustering algorithm can be used to classify hand gesture images in human-robot interaction application. However, FCM algorithm does not work well on those images in which noises exist. The noises or outliers make all the cluster centers towards to the center of all points. In this paper, a new FCM algorithm is proposed to detect the outliers and then make the outliers have no influence on centers calculation. The experiment shows that the new FCM algorithm can get more accurate centers than the traditional FCM algorithm.

From granulation-degranulation mechanisms to fuzzy rule-based models: Augmentation of granular-based models with a double fuzzy clustering

2021 ◽  
pp. 1-10
Author(s):  
Kaijie Xu ◽  
Hanyu E ◽  
Yinghui Quan ◽  
Ye Cui ◽  
Weike Nie
Keyword(s):  
Design Process ◽  
Fuzzy Clustering ◽  
Mathematical Proof ◽  
Experimental Studies ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Clustering Method ◽  
Rule Based ◽  
Information Granules ◽  
Fuzzy C Means

In this study, we develop a novel clustering with double fuzzy factors to enhance the performance of the granulation-degranulation mechanism, with which a fuzzy rule-based model is designed and demonstrated to be an enhanced one. The essence of the developed scheme is to optimize the construction of the information granules so as to eventually improve the performance of the fuzzy rule-based models. In the design process, a prototype matrix is defined to express the Fuzzy C-Means based granulation-degranulation mechanism in a clear manner. We assume that the dataset degranulated from the formed information granules is equal to the original numerical dataset. Then, a clustering method with double fuzzy factors is derived. We also present a detailed mathematical proof for the proposed approach. Subsequently, on the basis of the enhanced version of the granulation-degranulation mechanism, we design a granular fuzzy model. The whole design is mainly focused on an efficient application of the fuzzy clustering to build information granules used in fuzzy rule-based models. Comprehensive experimental studies demonstrate the performance of the proposed scheme.

GT2-CFC: General Type-2 Collaborative Fuzzy Clustering method

2021 ◽  
Author(s):  
Fariba Salehi ◽  
Mohammad Reza Keyvanpour ◽  
Arash Sharifi
Keyword(s):  
Fuzzy Clustering ◽  
General Type ◽  
Clustering Method ◽  
Fuzzy Clustering 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.

Sign in / Sign up

Export Citation Format

Share Document