A novel cluster validity criterion for fuzzy c-regression model clustering algorithm

2004 ◽  
Author(s):  
Chung-Chun Kung ◽  
Jui-Chun Hung
Keyword(s):  
Regression Model ◽  
Clustering Algorithm ◽  
Cluster Validity ◽  
Validity Criterion

scholarly journals Canonical PSO Based K-Means Clustering Approach for Real Datasets

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Lopamudra Dey ◽  
Sanjay Chakraborty
Keyword(s):  
Data Mining ◽  
Air Pollution ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Cluster Validity ◽  
Validity Assessment ◽  
Different Types ◽  
Clustering Approach ◽  
Validity Measure

“Clustering” the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.

Identification of Circulating Fluidized Bed Boiler Bed Temperature Based on Hyper-Plane-Shaped Fuzzy C-Regression Model

2020 ◽  
Vol 19 (04) ◽  
pp. 2050029
Author(s):  
Jianzhong Shi
Keyword(s):  
Regression Model ◽  
Fluidized Bed ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Circulating Fluidized Bed ◽  
Fuzzy Model ◽  
Identification Algorithm ◽  
Identification Process ◽  
Bed Temperature ◽  
Hyper Plane

Bed temperature in dense-phase zone is the key parameter of circulating fluidized bed (CFB) boiler for stable combustion and economic operation. It is difficult to establish an accurate bed temperature model as the complexity of circulating fluidized bed combustion system. T-S fuzzy model was widely applied in the system identification for it can approximate complex nonlinear system with high accuracy. Fuzzy c-regression model (FCRM) clustering based on hyper-plane-shaped distance has the advantages in describing T-S fuzzy model, and Gaussian function was adapted in antecedent membership function of T-S fuzzy model. However, Gaussian fuzzy membership function was more suitable for clustering algorithm using point to point distance, such as fuzzy c-means (FCM). In this paper, a hyper-plane-shaped FCRM clustering algorithm for T-S fuzzy model identification algorithm is proposed. The antecedent membership function of proposed identification algorithm is defined by a hyper-plane-shaped membership function and an improved fuzzy partition method is applied. To illustrate the efficiency of the proposed identification algorithm, the algorithm is applied in four nonlinear systems which shows higher identification accuracy and simplified identification process. At last, the algorithm is used in a circulating fluidized bed boiler bed temperature identification process, and gets better identification result.

A comparative study of different cluster validity indexes

2011 ◽  
Vol 34 (7) ◽  
pp. 876-890 ◽  
Author(s):  
Ruochen Liu ◽  
Xiaojuan Sun ◽  
Licheng Jiao ◽  
Yangyang Li
Keyword(s):  
Clustering Algorithm ◽  
Detailed Comparison ◽  
Synthetic Aperture ◽  
Data Sets ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Swarm Optimization ◽  
Radar Images ◽  
Comprehensive Comparison ◽  
Clustering Data

The cluster validity index plays an important role in most clustering algorithm based natural computations. So far, four typical cluster validity indexes have been proposed for clustering data with different structures, including the Euclid distance based Pakhira–Bandyopadhyay–Maulik index, the kernel function induced Chou–Su measure, the point symmetry distance based index and the manifold distance (MD) induced index. However, there is no detailed comparison made among these indexes. This paper compares these four cluster validity indexes by using a simple clustering technique based on particle swarm optimization (PSO). Extensive experiments on a large number of artificial synthesized data sets and UC Irvine data sets, texture images and synthetic-aperture radar images are performed in order to make a comprehensive comparison. Experimental results show that the PSO-based clustering algorithm using the MD induced index has a good performance on most of the data sets.

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