A Combinatorial Classifier for Error-Data in Joining Processes with Diverse-Granular Computing

2012 ◽  
Vol 548 ◽  
pp. 740-743
Author(s):  
Yi Lan Chen ◽  
Huan Bao Wang
Keyword(s):  
Fuzzy Clustering ◽  
Granular Computing ◽  
Classification Performance ◽  
Classification Model ◽  
Fuzzy Partition ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Hybrid Classification ◽  
Scale Granularity ◽  
Better Than

In this paper, we present a novel hybrid classification model with fuzzy clustering and design a newly combinatorial classifier for error-data in joining processes with diverse-granular computing, which is an ensemble of a naïve Bayes classifier with fuzzy c-means clustering. And we apply it to improve classification performance of traditional hard classifiers in more complex real-world situations. The fuzzy c-means clustering is applied to a fuzzy partition based on a given propositional function to augment the combinatorial classifier. This strategy would work better than a conventional hard classifier without fuzzy clustering. Proper scale granularity of objects contributes to higher classification performance of the combinatorial classifier. Our experimental results show the newly combinatorial classifier has improved the accuracy and stability of classification.

An Artificial Immune Inspired Hybrid Classification Algorithm and its Application to Fault Diagnosis

2011 ◽  
Vol 411 ◽  
pp. 626-629 ◽  
Author(s):  
Gang Li ◽  
Ming Yang ◽  
Jian Zhuang
Keyword(s):  
Information Entropy ◽  
Clonal Selection ◽  
Classification Algorithm ◽  
Classification Model ◽  
Superior Performance ◽  
Memory Cells ◽  
Artificial Immune ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Hybrid Classification

To efficiently mining the classification model, an artificial immune inspired hybrid classification algorithm was put forward by means of combining antibody clonal selection, fuzzy C means clustering (FCM) and information entropy principle. In this algorithm, fuzzy C means clustering algorithm was employed to generate initial antibody population for making use of the prior knowledge of the training data. From the viewpoint of information entropy, for evolving memory cells the information entropy of antibodies population was employed to provide stop criteria of training. Finally classification was performed in a nearest neighbor approach. Experimental results on the fault detection of DAMADICS demonstrate the effectiveness of the algorithm. Compared with CLONALG artificial immune classifiers, the hybrid classifier has a superior performance in terms of recognition rate, computation time, number of memory cells and condense rate.

scholarly journals MODEL PEMETAAN EVALUASI PENILAIAN KUALIFIKASI LULUSAN BERBASIS METODE FUZZY C_MEANS CLUSTERING

2014 ◽  
Vol 7 (2) ◽  
Author(s):  
Anif Hanifa Setianingrum
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Ag Cluster ◽  
Target Output

Dunia pendidikan sering mengalami masalah dengan tidak tercapainya tujuan yang telah ditetapkan dalam visi misi institusi. Banyak faktor yang menyebabkan tidak berjalan atau tidak tercapainya target output yang dihasilkan. Faktor-faktor internal SDM, metode pengajaran, serta kurikulum yang telah dirumuskan kadang tidak dapat memenuhi standarisasi kualifikasi dari pihak stakeholder. Metode evaluasi dan monitoring akan melakukan pemetaan permasalahan metode pengajaran dari para pelaksana institusi. Evaluasi Pemetaan dan Penerapan metode pengajaran dengan menggunakan Metode Fuzzy C-Means Clustering (FCM), dengan mengumpulkan data hasil penilaian dosen terhadap daftar nilai mahasiswa.. Penilaian juga harus dilakukan dengan hasil penilaian stakeholder.Hasil Cluster menyatakan ada Lima (5) cluster pengelompokkan Kualifikasi Mahasiswa (SO1, SO2, SO3) dan Identifikasi Penilaian SKKNI terhadap JRP  Cluster Pertama untuk K,V,AD,AG, Cluster Kedua  : D,H,O,W,AN, Cluster Ketiga untuk Mahasiswa A,M,R,T,AA,AJ, Cluster 4 Y,AC,AI,AK,AO, Cluster 5 E,I,J,N,AL.Ada persamaan dan ketidaksamaan nama mahasiswa dari hasil penilaian internal maupun hasil penilaian eksternal artinya Penilaian internal terhadap kualifikasi kelulusan mahasiswa berbeda dengan kriteria penilaian stakeholder terhadap standarisasi SKKNI.Kata Kunci: Fuzzy, Clustering, Standarisasi SKKNI, FCM

Fuzzy Clustering

2017 ◽  
pp. 40-61 ◽  
Author(s):  
Mashhour H. Baeshen ◽  
Malcolm J. Beynon ◽  
Kate L. Daunt
Keyword(s):  
Data Analysis ◽  
Service Quality ◽  
Mobile Phone ◽  
Fuzzy Clustering ◽  
Data Set ◽  
Clustering Techniques ◽  
Fuzzy C Means ◽  
Fuzzy Environment ◽  
External Variables ◽  
Fuzzy C Means Clustering

This chapter presents a study of the development of the clustering methodology to data analysis, with particular attention to the analysis from a crisp environment to a fuzzy environment. An applied problem concerning service quality (using SERVQUAL) of mobile phone users, and subsequent loyalty and satisfaction forms the data set to demonstrate the clustering issue. Following details on both the crisp k-means and fuzzy c-means clustering techniques, comparable results from their analysis are shown, on a subset of data, to enable both graphical and statistical elucidation. Fuzzy c-means is then employed on the full SERVQUAL dimensions, and the established results interpreted before tested on external variables, namely the level of loyalty and satisfaction across the different clusters established.

Fuzzy Clustering with Repulsive Prototypes

2010 ◽  
pp. 332-346
Author(s):  
Frank Rehm ◽  
Roland Winkler ◽  
Rudolf Kruse
Keyword(s):  
Data Analysis ◽  
Fuzzy Clustering ◽  
Experimental Results ◽  
Number Of Clusters ◽  
Fuzzy C Means ◽  
Analysis Process ◽  
Fuzzy C Means Clustering ◽  
The Right

A well known issue with prototype-based clustering is the user’s obligation to know the right number of clusters in a dataset in advance or to determine it as a part of the data analysis process. There are different approaches to cope with this non-trivial problem. This chapter follows the approach to address this problem as an integrated part of the clustering process. An extension to repulsive fuzzy c-means clustering is proposed equipping non-Euclidean prototypes with repulsive properties. Experimental results are presented that demonstrate the feasibility of the authors’ technique.

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.

Fuzzy Microaggregation for Microdata Protection

2003 ◽  
Vol 7 (2) ◽  
pp. 153-159 ◽  
Author(s):  
Josep Domingo-Ferrer ◽  
◽  
Vicenç Torra ◽  
Keyword(s):  
Fuzzy Clustering ◽  
Heuristic Methods ◽  
Fuzzy Partition ◽  
Fuzzy C Means ◽  
Protection Method ◽  
Np Problem

In this work we describe a microdata protection method based on the use of fuzzy clustering and, more specifically, using fuzzy c-means. Microaggregation is a well-known masking method for microdata protection used by National Statistical Offices. Given a set of objects described in terms of a set of variables, this method consists on building a partition of the objects and then replace the original evaluation for each variable by the aggregates of each partition. This is, the values in a given cluster are aggregated –fused– and used instead of the original ones. As the problem of finding the best partition for microdata protection is an NP problem, heuristic methods are considered in the literature. Our approach uses fuzzy c-means for building a fuzzy partition, instead of a crisp one.

A Gravitational Fuzzy C-Means Clustering Algorithm Based on Density Weight

2012 ◽  
Vol 182-183 ◽  
pp. 1681-1685
Author(s):  
Tian Wu Zhang ◽  
Gong Bing Guo
Keyword(s):  
Local Minimum ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Noise Data ◽  
Global Optimal ◽  
Minimum Points ◽  
Better Than

Fuzzy C-means clustering algorithm(FCM) is sensitive to its initialization of value and noise data and easy to fall into local minimum points, while it can’t get the global optimal solution. This paper introduces gravitation and density weight into the process of clustering, and proposes a gravitational Fuzzy C-Means clustering algorithm based on density weight (DWGFCM). The experimental results show that the algorithm has better global optimal solution, overcomes the shortcomings of traditional Fuzzy C-means clustering algorithm. Clustering results are obviously better than FCM algorithm.

FUZZY CLUSTERING WITH WEIGHTING OF DATA VARIABLES

2000 ◽  
Vol 08 (06) ◽  
pp. 735-746 ◽  
Author(s):  
ANNETTE KELLER ◽  
FRANK KLAWONN
Keyword(s):  
Objective Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Technique ◽  
Fuzzy C Means ◽  
Or Groups ◽  
Fuzzy C Means Clustering ◽  
Influence Parameter ◽  
Individual Variables ◽  
Single Data

We introduce an objective function-based fuzzy clustering technique that assigns one influence parameter to each single data variable for each cluster. Our method is not only suited to detect structures or groups of data that are not uniformly distributed over the structure's single domains, but gives also information about the influence of individual variables on the detected groups. In addition, our approach can be seen as a generalization of the well-known fuzzy c-means clustering algorithm.

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