scholarly journals Fuzzy clustering-based microaggregation to achieve probabilistic k-anonymity for data with constraints

2020 ◽  
Vol 39 (5) ◽  
pp. 5999-6008
Author(s):  
Vicenç Torra
Keyword(s):  
Fuzzy Clustering ◽  
Information Loss ◽  
Linear Constraints ◽  
Data Driven ◽  
Trade Off ◽  
Fuzzy C Means ◽  
Disclosure Risk ◽  
Protection Method ◽  
Good Trade

Microaggregation is an effective data-driven protection method that permits us to achieve a good trade-off between disclosure risk and information loss. In this work we propose a method for microaggregation based on fuzzy c-means, that is appropriate when there are constraints (linear constraints) on the variables that describe the data. Our method leads to results that satisfy these constraints even when the data to be masked do not satisfy them.

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.

scholarly journals Fuzzy Entropy-Based Spatial Hotspot Reliability

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 531
Author(s):  
Ferdinando Di Martino ◽  
Salvatore Sessa
Keyword(s):  
Clustering Algorithm ◽  
Geographical Area ◽  
Fuzzy Entropy ◽  
Circular Area ◽  
Analysis Problem ◽  
Trade Off ◽  
Fuzzy C Means ◽  
Good Trade ◽  
Disease Analysis ◽  
Fuzzy C Means Algorithm

Cluster techniques are used in hotspot spatial analysis to detect hotspots as areas on the map; an extension of the Fuzzy C-means that the clustering algorithm has been applied to locate hotspots on the map as circular areas; it represents a good trade-off between the accuracy in the detection of the hotspot shape and the computational complexity. However, this method does not measure the reliability of the detected hotspots and therefore does not allow us to evaluate how reliable the identification of a hotspot of a circular area corresponding to the detected cluster is; a measure of the reliability of hotspots is crucial for the decision maker to assess the need for action on the area circumscribed by the hotspots. We propose a method based on the use of De Luca and Termini’s Fuzzy Entropy that uses this extension of the Fuzzy C-means algorithm and measures the reliability of detected hotspots. We test our method in a disease analysis problem in which hotspots corresponding to areas where most oto-laryngo-pharyngeal patients reside, within a geographical area constituted by the province of Naples, Italy, are detected as circular areas. The results show a dependency between the reliability and fluctuation of the values of the degrees of belonging to the hotspots.

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.

Platinum(ii) acetylide complexes with star- and V-shaped configurations possessing good trade-off between optical transparency and optical power limiting performance

2017 ◽  
Vol 5 (45) ◽  
pp. 11672-11682 ◽  
Author(s):  
C. Yao ◽  
Z. Tian ◽  
D. Jin ◽  
F. Zhao ◽  
Y. Sun ◽  
...  
Keyword(s):  
Optical Power ◽  
Optical Transparency ◽  
Trade Off ◽  
Good Trade ◽  
Optical Power Limiting

Two series of Pt(ii) acetylide complexes containing dimesitylborane and phenyl terminal groups with star- and V-shaped configurations were synthesized.

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.

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.

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