Kajian Algoritma Fuzzy c-shell cluster Dalam Analisis Fuzzy Clustering Menggunakan Optimasi Pengganda Lagrange

In the group analysis (cluster analysis), there are several methods that have been developed, new methods which are much in demand is fuzzy clustering, fuzzy clustering in its development has been developed in a number of methods (algorithms) one of which is a method of fuzzy c-shell cluster .One method for algorithm to optimize the fuzzy c-shell clusters by using lagrange multipliers by means mendiferensialisasi the meter is in the fuzzy c-shell algorithm clusters. The main problem in the fuzzy c-shell algorithm clusters that determine the optimal parameter of the existing algorithms on the fuzzy c-shell cluster. This paper examines the methods of fuzzy c-shell clusters using lagrange multipliers in mathematics. The results showed that using lagrange multipliers obtained optimizationparameters by minimizing the objective functionlagrange multiplier function obtained using the optimum conditions for the parameters uik , vi  and ri as obtained in the above equation is: Keywords: cluster analysis, fuzzy clustering, fuzzy c-shell clusters, the lagrange multiplier

Adaptive fuzzy system for 3-D vision

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller

Classification of fresh edible oils using a coated piezoelectric sensor array-based electronic nose with soft computing approach for pattern recognition

An electronic nose based on an array of six bulk acoustic wave polymer-coated piezoelectric quartz (PZQ) sensors with soft computing-based pattern recognition was used for the classi-fication of edible oils. The electronic nose was presented with 346 samples of fresh edible oil headspace volatiles, generated at 45°C. Extra virgin olive (EVO), nonvirgin olive oil (NVO) and sunflower oil (SFO) were used over a period of 30 days. The sensor responses were visualized by plotting the results from principal component analysis (PCA). Classification of edible oils was carried out using fuzzy c-means as well as radial basis function (RBF) neural networks both from a raw data and data after having been preprocessed by fuzzy c-means. The fuzzy c-means results were poor (74%) due to the different cluster sizes. The result of RBF with fuzzy c-means preprocessing was 95% and 99% for raw data input. RBF networks with fuzzy c-means preprocessing provide the advantage of a simple architecture that is quicker to train.</p

An Intelligent System A Comparative Study Of Fuzzy C-Means And K-Means Clustering Techniques

Clustering analysis has been considered as useful means for identifying patterns of dataset. The aim for this analysis is to decide what is the most suitable algorithm to be used when dealings with new scatter data. In this analysis, two important clustering algorithms namely fuzzy c-means and k-means clustering algorithms are compared. These algorithms are applied to synthetic data 2-dimensional dataset. The numbers of data points as well as the number of clusters are determined, with that the behavior patterns of both the algorithm are analyzed. Quality of clustering is based on lowest distance and highest membership similarity between the points and the centre cluster in one cluster, known as inter-class cluster similarity. Fuzzy c-means and k-means clustering are compared based on the inter-class cluster similarity by obtaining the minimum value of summation of distance. Additionally, in fuzzy c-means algorithm, most researchers fix weighting exponent (m) to a conventional value of 2 which might not be the appropriate for all applications. In order to find m, also called as fuzziness coefficient, optimal in fuzzy c-means on particular dataset is based on minimal reconstruction error

Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset

A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value &lt; 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter m was evaluated to determine the distribution of membership values with variation in m from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters

Pengelompokan dan Analisis Pelanggan dengan menggunakan Fuzzy C-Means Clustering

ABSTRAKSI: Pada saat ini volume informasi pelanggan yang dimiliki oleh perusahaan semakin meningkat. Data mining dapat digunakan untuk menggali informasi berharga yang sebelumnya tidak diketahui dari suatu database pelanggan untuk mengetahui karakteristik pelanggan. Informasi tersebut dapat digunakan oleh perusahaan untuk melakukan manajemen pelanggan secara lebih efektif. Tugas akhir ini mengimplementasikan salah satu teknik data mining yaitu clustering untuk melakukan pengelompokan dan analisis pelanggan pada suatu perusahaan Telekomunikasi. Metode clustering yang digunakan adalah Fuzzy C-Means Clustering (FCM). Fuzzy C-Means Clustering adalah suatu teknik pengklasteran fuzzy dimana keberadaan tiap-tiap titik data dalam suatu klaster ditentukan oleh derajat keanggotaan. Karena metode clustering bersifat unsupervised learning maka digunakan cluster validity index untuk menganalisis kualitas hasil segmentasi. Dari hasil yang didapat menunjukkan bahwa Fuzzy C-Means Clustering dapat digunakan untuk melakukan segmentasi pelanggan. Namun dalam penentuan validasi klaster menggunakan cluster validity index untuk fuzzy clustering, perlu menggunakan variasi parameter masukan yang berbeda-beda untuk menentukan hasil segmentasi yang optimal.Kata Kunci : data mining, fuzzy c-means, clustering, pengelompokan pelanggan,ABSTRACT: Nowadays, the amount of customer information that companies have, has increased significantly. Data mining can be used to retrieve valuable informations that are previously unknown to retrieve the customer characteristics. These informations can be used by the company to effectively carry out customer management. This final project implements one of the techniques in data mining, which is clustering to execute customer segmentation and analysis. The clustering method that is used is Fuzzy C-Means Clustering. Fuzzy C-Means Clustering is a fuzzy clustering technique where each data membership in a cluster is determined by a membership degree. Because clustering is an unsupervised method, cluster validity index is used to analyze the quality of the segmentation results.The testing results shows that Fuzzy C-Means Clustering can be used for customer segmentation. However in defining cluster validation using cluster validity index for fuzzy clustering, it is necessary to use a variety of input parameter for defining the optimum segmentation results.Keyword: data mining, fuzzy c-means, clustering, customer segmentation, cluste

PENGELOMPOKAN KABUPATEN/KOTA DI PULAU KALIMANTAN BERDASARKAN INDIKATOR KESEJAHTERAAN RAKYAT MENGGUNAKAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS

Cluster analysis has the aim of grouping several objects of observation based on the data found in the information to describe the objects and their relationships. The grouping method used in this research is the Fuzzy C-Means (FCM) and Subtractive Fuzzy C-Means (SFCM) methods. The two grouping methods were applied to the people's welfare indicator data in 42 regencies/cities on the island of Kalimantan. The purpose of this study was to obtain the results of grouping districts/cities on the island of Kalimantan based on indicators of people's welfare and to obtain the results of a comparison of the FCM and SFCM methods. Based on the results of the analysis, the FCM and SFCM methods yield the same conclusions, so that in this study the FCM and SFCM methods are both good to use in classifying districts/cities on the island of Kalimantan based on people's welfare indicators and produce an optimal cluster of two clusters, namely the first cluster consisting of 10 Regencies/Cities on the island of Kalimantan, while the second cluster consists of 32 districts/cities on the island of Borneo.Analisis klaster mempunyai tujuan untuk mengelompokkan beberapa objek pengamatan berdasarkan data yang ditemukan dalam informasi untuk menggambarkan objek dan hubungannya. Metode pengelompokan yang digunakan dalam penelitian ini adalah metode Fuzzy C-Means (FCM) dan Subtractive Fuzzy C-Means (SFCM). Dua metode pengelompokan tersebut diterapkan pada data indikator kesejahteraan rakyat pada 42 Kabupaten/Kota di Pulau Kalimantan. Tujuan dari penelitian ini adalah untuk mendapatkan hasil pengelompokan Kabupaten/Kota di Pulau Kalimantan berdasarkan indikator kesejahteraan rakyat serta untuk memperoleh hasil perbandingan metode FCM dan SFCM. Berdasarkan hasil analisis, metode FCM dan SFCM menghasilkan kesimpulan yang sama sehingga pada penelitian ini metode FCM dan SFCM sama-sama baik untuk digunakan dalam mengelompokkan Kabupaten/Kota di Pulau Kalimantan berdasarkan indikator kesejahteraan rakyat dan menghasilkan klaster optimal sebanyak dua klaster yaitu klaster pertama beranggotakan 10 Kabupaten/Kota di Pulau Kalimantan sedangkan klaster kedua beranggotakan 32 Kabupaten/Kota di Pulau Kalimantan

Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602