An output‐oriented classification of multiple attribute decision‐making techniques based on fuzzy c ‐means clustering method

2017 ◽  
Vol 26 (6) ◽  
pp. 2476-2493 ◽  
Author(s):  
Ezzatollah Asgharizadeh ◽  
Mohammadreza Taghizadeh Yazdi ◽  
Abdolkarim Mohammadi Balani
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
Multiple Attribute ◽  
Fuzzy C Means Clustering

scholarly journals Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform

Computation ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 54 ◽  
Author(s):  
Anbu ◽  
Thangavelu ◽  
Ashok
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Rule ◽  
Discrete Wavelet ◽  
Clustering Method ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

scholarly journals Neutrosophic Cubic Einstein Hybrid Geometric Aggregation Operators with Application in Prioritization Using Multiple Attribute Decision-Making Method

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 346 ◽  
Author(s):  
Gulistan ◽  
Khan ◽  
Kadry ◽  
Alhazaymeh
Keyword(s):  
Decision Making ◽  
Everyday Life ◽  
Decision Maker ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Neutrosophic Set ◽  
Multiple Attribute ◽  
Life Issue

Viable collection is one of the imperative instruments of decision-making hypothesis. Collection operators are not simply the operators that normalize the value; theyrepresent progressively broad values that can underline the entire information. Geometric weighted operators weight the values only, andthe ordered weighted geometric operators weight the ordering position only.Both of these operators tend to the value that relates to the biggest weight segment. Hybrid collection operators beat these impediments of weighted total and request total operators. Hybrid collection operators weight the incentive as well as the requesting position. Neutrosophic cubic sets (NCs) are a classification of interim neutrosophic set and neutrosophic set. This distinguishing of neutrosophic cubic set empowers the decision-maker to manage ambiguous and conflicting data even more productively. In this paper, we characterized neutrosophic cubic hybrid geometric accumulation operator (NCHG) and neutrosophic cubic Einstein hybrid geometric collection operator (NCEHG). At that point, we outfitted these operators upon an everyday life issue which empoweredus to organize the key objective to develop the industry.

A Fuzzy C-Means Clustering Method Based for 3D Mechanical Models Retrieval

2011 ◽  
Vol 201-203 ◽  
pp. 147-150
Author(s):  
Hui Jing ◽  
Cong Li ◽  
Bing Kuang ◽  
Mei Fa Huang ◽  
Fu Yun Liu
Keyword(s):  
Clustering Method ◽  
Shape Distribution ◽  
Fuzzy C Means ◽  
Mechanical Models ◽  
Fuzzy C Means Clustering ◽  
Model Database ◽  
The Given ◽  
Query Model

To accelerate the development of new product, one important method is to use the information of the existed products. In this paper, Fuzzy C-Means clustering method (FCM) is proposed to retrieve the desired model in the given model database. In the retrieval process, first, the shape distribution histograms of the model are established and transformed into the proper format of FCM. Then, the shape distribution histograms of the model are served as inputs of FCM and classified into several groups by this algorithm. Last, all the models that belong to the classification of the query model are returned as the retrieval results. To validate the proposed method, a case study is presented. The results show that the result of proposed method is better than that of Shape Distribution. Thus, this method is suitable for 3D mechanical models retrieval.

scholarly journals Application of Fuzzy C-Means Clustering Method Using Matlab To Map the Potential of Rice Plant In Bekasi Regency

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Winarni Suwarso
Keyword(s):  
Data Mining ◽  
Rice Plant ◽  
Food Crops ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Rice Yields ◽  
Clustering Data ◽  
Fuzzy C Means Algorithm

Abstract Based on the data of rice crops from BPS-Statistics of Bekasi Regency in the field of Food Crops, there are several sub-districts in Bekasi Regency with varying rice yields. Therefore, it is necessary to group the sub-districts with the highest potential of rice producers. Therefore, a method is needed to facilitate the classification of paddy producing districts. By Fuzzy C-Means clustering method, the division of rice-producing sub-districts can be done based on the area of rice harvest (Ha) and rice production (ton). In this research, clustering of potential sub-districts using the Fuzzy C-Means algorithm is aimed at facilitating the grouping of a sub-district with the largest and low rice yields. The result is an illustration that shows the subdistrict grouping based on the results of paddy farming. Keywords: Clustering, Data Mining, Fuzzy C-Means Algorithm

PENERAPAN METODE SIMPLE ADDITIVE WEIGHTING DALAM PENETAPAN TENAGA KEPENDIDIKAN BERPRESTASI

2018 ◽  
Vol 1 (2) ◽  
pp. 45-54
Author(s):  
Helpi Nopriandi
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Multiple Attribute Decision Making ◽  
Multiple Attribute ◽  
Simple Additive Weighting

Tenaga Kependidikan merupakan anggota masyarakat yang mengabdikan diri dan diangkat untuk menunjang penyelenggaraan pendidikan. Decision Support Systems atau lebih dikenal dengan Sistem Pendukung Keputusan adalah bagian dari sebuah sistem informasi yang berbasis komputer termasuk sistem yang berbasis ilmu pengetahuan dan dipakai untuk mendukung pengambil  keputusan dalam suatu organisasi atau perusahaan. untuk memudahkan pimpinan dalam mengambil sebuah keputusan dibuatlah suatu sistem pengambil keputusan dengan menggunakan Fuzzy Multiple Attribute Decision Making  (FMADM) digunakan untuk mencari alternatif optimalkan dari sejumlah alternatif dengan kriteria tertentu, sedangkan metode Simple Additive Weighting (SAW). Metode SAW sering juga dikenal istilah metode penjumlahan terbobot. Konsep dasar metode SAW adalah mencari penjumlahan terbobot dari rating kinerja pada setiap alternatif dari semua atribut.

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