Results 31 to 40 of about 172,977 (352)
Combination Evaluation Method of Fuzzy C-Mean Clustering Validity Based on Hybrid Weighted Strategy
IEEE Access, 2021 Clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clustering validity are based on single clustering validity function.
H. Y. Wang, J. S. Wang, G. Wang
doaj +1 more source
Extended incremental fuzzy clustering algorithm for sparse high-dimensional big data [PDF]
Jisuanji gongcheng, 2019 Fuzzy C-Means(FCM) clustering algorithm can only deal with low-dimensional data and is sensitive to the initial center,without considering the interactions between class centers.For this reason,an improved method of initial center selection is designed ...
QIAN Xuezhong,YAO Linya
doaj +1 more source
Soft ranking in clustering [PDF]
, 2009 Due to the diffusion of large-dimensional data sets (e.g., in DNA microarray or document organization and retrieval applications), there is a growing interest in clustering methods based on a proximity matrix. These have the advantage of being based on a
Aggarwal +17 more
core +1 more source
Stock Data Clustering of Food and Beverage Company
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2007 Cluster analysis can be defined as identifying groups of similar objects to discover distribution of patterns and interesting correlations in large data sets.
Shofwatul Uyun, Subanar Subanar
doaj +1 more source
BigFCM: Fast, Precise and Scalable FCM on Hadoop
, 2016 Clustering plays an important role in mining big data both as a modeling technique and a preprocessing step in many data mining process implementations.
Ghadiri, Nasser +2 more
core +1 more source
TOWARDS FINDING A NEW KERNELIZED FUZZY C-MEANS CLUSTERING ALGORITHM [PDF]
Journal of Process Management and New Technologies, 2014 Kernelized Fuzzy C-Means clustering technique is an attempt to improve the performance of the conventional Fuzzy C-Means clustering technique. Recently this technique where a kernel-induced distance function is used as a similarity measure instead ...
Samarjit Das, Hemanta K. Baruah
doaj
ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS
Media Statistika, 2015 Fuzzy C-Means (FCM) is one of the most frequently used clustering method. However FCM has some disadvantages such as number of clusters to be prespecified and partition matrix to be randomly initiated which makes clustering result becomes inconsistent ...
Baiq Nurul Haqiqi, Robert Kurniawan
doaj +1 more source
Accelerating fuzzy clustering [PDF]
Information Sciences, 2009 This paper extends earlier work [C. Borgelt, R. Kruse, Speeding up fuzzy clustering with neural network techniques, in: Proceedings of the 12th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'03, St. Louis, MO, USA), IEEE Press, Piscataway, NJ, USA, 2003] on an approach to accelerate fuzzy clustering by transferring methods that were ...
openaire +1 more source
Assessment of Heart Disease using Fuzzy Classification Techniques
The Scientific World Journal, 2001 In this paper we discuss the classification results of cardiac patients of ischemical cardiopathy, valvular heart disease, and arterial hypertension, based on 19 characteristics (descriptors) including ECHO data, effort testings, and age and weight.
Horia F. Pop, Tudor L. Pop, Costel Sarbu
doaj +1 more source
Chemical Engineering Transactions, 2017 In this paper, the author research on electrical equipment’s fault diagnosis based on the improved support vector machine and fuzzy clustering. Combining the support vector combined fuzzy sets and neural network to carry on the fault diagnosis is a most ...
Yuling Yan
doaj +1 more source