scholarly journals Extracting physical homogeneous regions out of irrigation networks using fuzzy clustering method: a case study for the Ghazvin canal irrigation network

2010 ◽  
Vol 13 (4) ◽  
pp. 652-660 ◽  
Author(s):  
M. J. Monem ◽  
S. M. Hashemy
Keyword(s):  
Fuzzy Clustering ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Spatial Diversity ◽  
Optimal Number ◽  
Irrigation Network ◽  
Irrigation Networks ◽  
Fuzzy Clustering Method ◽  
Physical Attributes ◽  
Homogeneous Regions

Improving the current operation and maintenance activities is one of the main steps in achieving higher performance of irrigation networks. Improving the irrigation network management, influenced by different spatial and temporal parameters, is confronted with special difficulties. One of the controversial issues often faced by decision-makers is how to cope with the spatial diversity of irrigation systems. Homogeneous area detection out of the irrigation networks could improve the current management of networks. The idea behind this research is to present a quantitative benchmark for exploring the homogeneous areas with similar physical attributes out of the network region. Five physical attributes, such as length, capacity, number of intakes, number of conveyance structures and the covered irrigated area for each canal reach, are used for spatial clustering. Two fuzzy clustering algorithms, namely FCM and GK, are applied to the Ghazvin irrigation network. Using a clustering validity index, SC, shows that the GK algorithm is the more appropriate tool for clustering of the considered dataset. According to the results the optimal number of clusters for the Ghazvin irrigation project is derived as nine clusters and the irrigated district is classified into nine homogeneous areas. Physical homogeneous regions provide a context for better and easier decision-making.

GIVING FUZZINESS TO SPATIAL CLUSTERS: A NEW INDEX FOR CHOOSING THE OPTIMAL NUMBER OF CLUSTERS

2013 ◽  
Vol 22 (03) ◽  
pp. 1350009 ◽  
Author(s):  
GEORGE GREKOUSIS
Keyword(s):  
Fuzzy Clustering ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Fuzzy Cluster ◽  
Cluster Validation ◽  
Number Of Clusters ◽  
A Value ◽  
Membership Value ◽  
Optimal Number Of Clusters

Choosing the optimal number of clusters is a key issue in cluster analysis. Especially when dealing with more spatial clustering, things tend to be more complicated. Cluster validation helps to determine the appropriate number of clusters present in a dataset. Furthermore, cluster validation evaluates and assesses the results of clustering algorithms. There are numerous methods and techniques for choosing the optimal number of clusters via crisp and fuzzy clustering. In this paper, we introduce a new index for fuzzy clustering to determine the optimal number of clusters. This index is not another metric for calculating compactness or separation among partitions. Instead, the index uses several existing indices to give a degree, or fuzziness, to the optimal number of clusters. In this way, not only do the objects in a fuzzy cluster get a membership value, but the number of clusters to be partitioned is given a value as well. The new index is used in the fuzzy c-means algorithm for the geodemographic segmentation of 285 postal codes.

scholarly journals An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ze Dong ◽  
Hao Jia ◽  
Miao Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Majority Vote ◽  
Clustering Algorithms ◽  
Nsga Ii ◽  
Number Of Clusters ◽  
Automatic Data ◽  
Multiobjective Genetic Algorithm ◽  
Fuzzy Clustering Method

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

scholarly journals A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters

2017 ◽  
Vol 65 (4) ◽  
pp. 359-365 ◽  
Author(s):  
Javier Senent-Aparicio ◽  
Jesús Soto ◽  
Julio Pérez-Sánchez ◽  
Jorge Garrido
Keyword(s):  
Frequency Analysis ◽  
Fuzzy Clustering ◽  
Optimal Number ◽  
Regional Frequency Analysis ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Homogeneous Regions ◽  
Optimal Number Of Clusters

AbstractOne of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis.

scholarly journals Cluster Validity Index to Determine the Optimal Number Clusters of Fuzzy Clustering for Classify Customer Buying Behavior

2021 ◽  
Vol 5 (1) ◽  
pp. 7-12
Author(s):  
Salnan Ratih Asrriningtias
Keyword(s):  
Fuzzy Clustering ◽  
Optimal Number ◽  
Buying Behavior ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Validity Index ◽  
Number Of Clusters ◽  
Best Value ◽  
Fuzzy Clustering Method ◽  
The Right

One of the strategies in order to compete in Batik  MSMEs  is to look at the characteristics of the customer. To make it easier to see the characteristics of  customer buying behavior, it is necessary to classify customers based on similarity of characteristics using fuzzy clustering. One of the parameters that must be determined at the beginning of the fuzzy clustering method is the number of clusters. Increasing the number of clusters does not guarantee the best performance, but the right number of clusters greatly affects the performance of fuzzy clustering. So to get optimal number cluster, we can measured the result of clustering in each number cluster using the cluster validity index. From several types of cluster validity index,  NPC give the best value. Optimal number cluster that obtained by the validity index is 2 and this number cluster give classify result with small variance value

Efficient Optimization Formulation Through Variable Reduction for Clustering Algorithms

2018 ◽  
pp. 135-162
Author(s):  
Priyanka Devi Pantula ◽  
Srinivas Soumitri Miriyala ◽  
Kishalay Mitra
Keyword(s):  
Optimization Problem ◽  
Clustering Algorithms ◽  
Problem Formulation ◽  
Optimal Number ◽  
Learning Methods ◽  
Decision Variables ◽  
Fuzzy Clustering Method ◽  
Variable Reduction ◽  
Simple Implementation ◽  
Computationally Intensive

Clustering based unsupervised learning methods are gaining importance in the field of data analytics, owing to their high accuracy, simple implementation and fast computation when compared with conventional supervised learning methods. Among several types of clustering techniques, those implying optimization routines are found to be more efficient. However, explosion in number of decision variables is making these algorithms computationally intensive. The authors present an efficient two stage optimization problem formulation based fuzzy clustering method which works through variable reduction approach. The membership values associated with each data point, forming the majority of decision variables are estimated as parameterized network outputs similar to ANNs. The reduction in decision variables allows the implementation of evolutionary optimization solvers to solve the multi objective optimization problem for clustering. Further the second stage optimization problem estimates the optimal number of clusters. Results with prominent case studies from literature are presented

Decision Theory, an Unprecedented Validation Scheme for Rough-Fuzzy Clustering

2016 ◽  
Vol 25 (02) ◽  
pp. 1650003
Author(s):  
S. Revathy ◽  
B. Parvathavarthini ◽  
S. Shiny Caroline
Keyword(s):  
Decision Theory ◽  
Fuzzy Clustering ◽  
Risk Measure ◽  
Clustering Algorithms ◽  
Cluster Structure ◽  
Optimal Number ◽  
Cluster Validation ◽  
Novel Approach ◽  
Validity Measure ◽  
Validation Scheme

Cluster validation is an essential technique in all cluster applications. Several validation methods measure the accuracy of cluster structure. Typical methods are geometric, where only distance and membership form the core of validation. Yao's decision theory is a novel approach for cluster validation, which evolved loss calculations and probabilistic based measure for determining the cluster quality. Conventional rough set algorithms have utilized this validity measure. This paper propagates decision theory, an unprecedented validation scheme for Rough-Fuzzy clustering by resolving loss and probability calculations to predict the risk measure in clustering techniques. Experiments with synthetic and UCI datasets have been performed, proven to deduce the optimal number of clusters overcoming the downsides of traditional validation frameworks. The proposed index can also be applied to other clustering algorithms and extends the usefulness in business oriented data mining.

A Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Automatic Identification of Clusters

2016 ◽  
Vol 15 (05) ◽  
pp. 949-974 ◽  
Author(s):  
Si He ◽  
Nabil Belacel ◽  
Alan Chan ◽  
Habib Hamam ◽  
Yassine Bouslimani
Keyword(s):  
Simulated Annealing ◽  
Fuzzy Clustering ◽  
A Priori ◽  
Clustering Algorithms ◽  
Hybrid Approach ◽  
Real Life ◽  
Automatic Identification ◽  
Artificial Fish Swarm Algorithm ◽  
Fuzzy Clustering Method ◽  
Artificial Fish Swarm

This paper introduces an alternative fuzzy clustering method that does not require fixing the number of clusters a priori and produce reliable clustering results. This newly proposed method empowers the existing Improved Artificial Fish Swarm algorithm (IAFSA) by the simulated annealing (SA) algorithm. The hybrid approach can prevent IAFSA from unexpected vibration and accelerate convergence rate in the late stage of evolution. Computer simulations are performed to compare this new method with well-known fuzzy clustering algorithms using several synthetic and real-life datasets. Our experimental results show that our newly proposed approach outperforms some other well-known existing fuzzy clustering algorithms in terms of both accuracy and robustness.

scholarly journals Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
David J. Miller ◽  
Carl A. Nelson ◽  
Molly Boeka Cannon ◽  
Kenneth P. Cannon
Keyword(s):  
Fuzzy Clustering ◽  
Real World ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Optimum Number ◽  
Clustering Methods ◽  
Real World Data ◽  
Data Set ◽  
World Data

Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine how many clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzyα-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that theα-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysis

scholarly journals Fuzzy Clustering in Assortative and Disassortative Networks

2021 ◽  
Vol 25 (6) ◽  
pp. 989-999
Author(s):  
Ryoichi Kojima ◽  
Roberto Legaspi ◽  
Toshiaki Murofushi ◽  
◽  
Keyword(s):  
Fuzzy Clustering ◽  
Real World ◽  
Synthetic Data ◽  
Optimal Number ◽  
Clustering Method ◽  
Fuzzy Clustering Method ◽  
High Degree ◽  
Assortative Network ◽  
Better Than ◽  
Optimal Number Of Clusters

Despite the significance of assortativity as a property of networks that paves for the emergence of new structural types, surprisingly, there has been little research done on assortativity. Assortative networks are perhaps among the most prominent examples of complex networks believed to be governed by common phenomena, thereby producing structures far from random. Further, certain vertices possess high centrality and can be regarded as significant and influential vertices that can become cluster centers that connect with high membership to many of the surrounding vertices. We propose a fuzzy clustering method to meaningfully characterize assortative, as well as disassortative, networks by adapting the Bonacichi’s power centrality to seek the high degree centrality vertices to become cluster centers. Moreover, we leverage our novel modularity function to determine the optimal number of clusters, as well as the optimal membership among clusters. However, due to the difficulty of finding real-world assortative network datasets that come with ground truths, we evaluated our method using synthetic data but possibly bearing resemblance to real-world network datasets as they were generated by the Lancichinetti–Fortunato–Radicchi benchmark. Our results show our non-hierarchical method outperforms a known hierarchical fuzzy clustering method, and also performs better than a well-known membership-based modularity function. Our method proved to perform beyond satisfactory for both assortative and disassortative networks.

scholarly journals Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880353 ◽  
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Dejian Sun ◽  
Wei Wang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithms ◽  
Three Dimensional ◽  
Degradation Process ◽  
Performance Degradation ◽  
Mechanical Equipment ◽  
Time Aggregation ◽  
Fuzzy Clustering Method ◽  
Basic Scale ◽  
Fuzzy C Means Clustering

A method based on basic scale entropy and Gath-Geva fuzzy clustering is proposed in order to solve the issue of bearing degradation condition recognition. The evolution rule of basic scale entropy for bearing in performance degradation process is analyzed first, and the monotonicity and sensitivity of basic scale entropy are emphasized. Considering the continuity of the bearing degradation condition at the time scale, three-dimensional degradation eigenvectors are constructed including basic scale entropy, root mean square, and degradation time, and then, Gath-Geva fuzzy clustering method is used to divide different conditions in performance degradation process, thus realizing performance degradation recognition for bearing. Bearing whole lifetime data from IEEE PHM 2012 is adopted in application and discussion, and fuzzy c-means clustering and Gustafson–Kessel clustering algorithms are analyzed for comparison. The results show that the proposed basic scale entropy-Gath-Geva method has better clustering effect and higher time aggregation than the other two algorithms and is able to provide an effective way for mechanical equipment performance degradation recognition.

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