scholarly journals PENERAPAN METODE FUZZY C-MEANS PADA PENGELOMPOKAN PASIEN KANKER PAYUDARA PASCA OPERASI MENGGUNAKAN HABERMAN’S SURVIVAL DATASET

2018 ◽  
Vol 1 (2) ◽  
pp. 42-46
Author(s):  
Hetty Rohayani AH ◽  
Afrizal J
Keyword(s):  
Fuzzy Clustering ◽  
Data Set ◽  
Fuzzy C Means

Kanker payudara adalah penyakit yang merupakan urutan kedua sebagai penyebab kematian di dunia. Dimana penanganan kanker payudara diantaranya dilakukan dengan operasi, tetapi penanganan kanker payudara dengan jalan operasi tidaklah merupakan suatu jalan mengatasi masalah, karena kemudian masalah yang timbul adalah kebertahanan hidup pasien pasca dilakukannya operasi. Dalam penelitian ini dengan menggunakan metode fuzzy clustering means mencoba mengelompokan pasien kanker payudara pasca dilakukan operasi, dimana pengelompokan dijadikan dua kelompok yang pertama adalah kebertahanan hidup pasien kanker payudara pasca operasi lebih dari lima tahun dan kebertahanan hidup pasien pasca operasi kanker payudara yang kurang dari lima tahun. Dihasilkan pengelompokan berdasarkan atribut yang meliputi usia saat pasien terkena kanker, tahun dilakukannya operasi dan identitas secara medis. Yang mana berhasil mengelompokan data yang berasal dari data set haberman survival menjadi dua kelompok.

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.

Construct Knowledge Structure of Linear Algebra

2011 ◽  
Vol 211-212 ◽  
pp. 793-797
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih ◽  
Sue Fen Huang
Keyword(s):  
Knowledge Management ◽  
Linear Algebra ◽  
Fuzzy Clustering ◽  
Mahalanobis Distance ◽  
Clustering Algorithms ◽  
Knowledge Structure ◽  
Interpretive Structural Modeling ◽  
Cognitive Characteristics ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Apply interpretive structural modeling to construct knowledge structure of linear algebra. New fuzzy clustering algorithms improved fuzzy c-means algorithm based on Mahalanobis distance has better performance than fuzzy c-means algorithm. Each cluster of data can easily describe features of knowledge structures individually. The results show that there are six clusters and each cluster has its own cognitive characteristics. The methodology can improve knowledge management in classroom more feasible.

Parameter Prediction Based on Type-2 Fuzzy Clustering

2018 ◽  
Vol 26 (06) ◽  
pp. 877-892 ◽  
Author(s):  
Türkan Erbay Dalkiliç ◽  
Seda Sağirkaya
Keyword(s):  
Fuzzy Logic ◽  
Regression Analysis ◽  
Fuzzy Clustering ◽  
Alternative Methods ◽  
Data Set ◽  
Clustering Problem ◽  
Fuzzy Clustering Method ◽  
Parameter Prediction

In regression analysis, the data have different distributions which requires to go beyond the classical analysis during the prediction process. In such cases, the analysis method based on fuzzy logic is preferred as alternative methods. There are couple important steps in the regression analysis based on fuzzy logic. One of them is identification of the clusters that generate the data set, the other is the degree of memberships that are determined the grades of the contributions of the data contained in these clusters. In this study, parameter prediction based on type-2 fuzzy clustering is discussed. Firstly, type-1 fuzzy clustering problem was solved by the fuzzy c-means (FCM) method when the fuzzifier index is equal to two. Then the fuzzifier index m is defined as interval number. The membership degrees to the sets are determined by type-2 fuzzy clustering method. Membership degree obtained as a result of clustering based on type-1 and type-2 fuzzy logic are used as weight and parameter prediction using these membership degrees that determined by the proposed algorithm. Finally, the prediction result of the type-1 and type-2 fuzzy clustering parameter is compared with the error criterion based on the difference between observed values and the predicted values.

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

Wavelet-Based Immune Fuzzy C-Means Algorithm

2014 ◽  
Vol 685 ◽  
pp. 638-641
Author(s):  
Zhi Xin Ma ◽  
Bin Bin Wen ◽  
Da Gan Nie
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Clustering ◽  
Spatial Data ◽  
Reproductive Mode ◽  
Descent Direction ◽  
Fuzzy C Means ◽  
Objective Function Values ◽  
Data Objects ◽  
Fuzzy C Means Algorithm ◽  
Convergence Of Algorithm

Fuzzy clustering can express the ambiguity ofsample category, and better reflect the actual needs of datamining. By introducing wavelet transform and artificial immunealgorithm to fuzzy clustering, Wavelet-based Immune Fuzzy C-means Algorithm (WIFCM) is proposed for overcoming theimperfections of fuzzy clustering, such as falling easily into localoptimal solution, slower convergence speed and initialization-dependence of clustering centers. Innovations of WIFCM arethe elite extraction operator and the descent reproductive mode.Using the locality and multi-resolution of wavelet transform, theelite extraction operator explores the distribution and densityinformation of spatial data objects in multi-dimensional spaceto guide the search of cluster centers. Taking advantage ofthe relationship between the relative positions of elite centersand inferior centers, the descent reproductive mode obtains theapproximate fastest descent direction of objective function values,and assures fast convergence of algorithm. Compared to theclassic fuzzy C-means algorithm, experiments on 3 UCI data setsshow that WIFCM has obvious advantages in average numberof iterations and accuracy.

The Application of Fuzzy Clustering Number Algorithm in Network Intrusion Detection

2013 ◽  
Vol 760-762 ◽  
pp. 2220-2223
Author(s):  
Lang Guo
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Local Optimum ◽  
Cluster Number ◽  
Data Set ◽  
Network Intrusion ◽  
Correlation Degree ◽  
Indicator Data ◽  
Detection Effect

In view of the defects of K-means algorithm in intrusion detection: the need of preassign cluster number and sensitive initial center and easy to fall into local optimum, this paper puts forward a fuzzy clustering algorithm. The fuzzy rules are utilized to express the invasion features, and standardized matrix is adopted to further process so as to reflect the approximation degree or correlation degree between the invasion indicator data and establish a similarity matrix. The simulation results of KDD CUP1999 data set show that the algorithm has better intrusion detection effect and can effectively detect the network intrusion data.

A scalable eigenspace-based fuzzy c-means for topic detection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hendri Murfi
Keyword(s):  
Representation Learning ◽  
Detection Methods ◽  
Data Sets ◽  
Topic Detection ◽  
Data Set ◽  
Content Type ◽  
Running Time ◽  
Fuzzy C Means ◽  
Coherence Score ◽  
Value Decomposition

PurposeThe aim of this research is to develop an eigenspace-based fuzzy c-means method for scalable topic detection.Design/methodology/approachThe eigenspace-based fuzzy c-means (EFCM) combines representation learning and clustering. The textual data are transformed into a lower-dimensional eigenspace using truncated singular value decomposition. Fuzzy c-means is performed on the eigenspace to identify the centroids of each cluster. The topics are provided by transforming back the centroids into the nonnegative subspace of the original space. In this paper, we extend the EFCM method for scalability by using the two approaches, i.e. single-pass and online. We call the developed topic detection methods as oEFCM and spEFCM.FindingsOur simulation shows that both oEFCM and spEFCM methods provide faster running times than EFCM for data sets that do not fit in memory. However, there is a decrease in the average coherence score. For both data sets that fit and do not fit into memory, the oEFCM method provides a tradeoff between running time and coherence score, which is better than spEFCM.Originality/valueThis research produces a scalable topic detection method. Besides this scalability capability, the developed method also provides a faster running time for the data set that fits in memory.

Cluster Analysis with Various Algorithms for Mixed Data

2017 ◽  
pp. 282-317
Author(s):  
Abha Sharma ◽  
R. S. Thakur
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithms ◽  
Complex Problem ◽  
Experimental Results ◽  
Mixed Data ◽  
Data Set ◽  
Fuzzy C Means ◽  
Conversion Method ◽  
Inherent Structure ◽  
Numeric Data

Analyzing clustering of mixed data set is a complex problem. Very useful clustering algorithms like k-means, fuzzy c-means, hierarchical methods etc. developed to extract hidden groups from numeric data. In this paper, the mixed data is converted into pure numeric with a conversion method, the various algorithm of numeric data has been applied on various well known mixed datasets, to exploit the inherent structure of the mixed data. Experimental results shows how smoothly the mixed data is giving better results on universally applicable clustering algorithms for numeric data.

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.

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