Non-Euclidean Extension of FCMdd-Based Linear Clustering for Relational Data

2011 ◽  
Vol 15 (8) ◽  
pp. 1050-1056 ◽  
Author(s):  
Takeshi Yamamoto ◽  
◽  
Katsuhiro Honda ◽  
Akira Notsu ◽  
Hidetomo Ichihashi
Keyword(s):  
Fuzzy Clustering ◽  
Real World ◽  
Numerical Experiments ◽  
Document Classification ◽  
Classification Task ◽  
Relational Data ◽  
Real World Applications ◽  
Data Elements ◽  
Euclidean Type ◽  
Linear Cluster

Relational data is common in many real-world applications. Linear fuzzy clustering models have been extended for handling relational data based on Fuzzyc-Medoids (FCMdd) framework. In this paper, with the goal being to handle non-Euclidean data, β-spread transformation of relational data matrices used in Non-Euclidean-type Relational Fuzzy (NERF)c-means is applied before FCMdd-type linear cluster extraction. β-spread transformation modifies data elements to avoid negative values for clustering criteria of distances between objects and linear prototypes. In numerical experiments, typical features of the proposed approach are demonstrated not only using artificially generated data but also in a document classification task with a document-keyword co-occurrence relation.

scholarly journals A Comparative Study on TIBA Imputation Methods in FCMdd-Based Linear Clustering with Relational Data

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Takeshi Yamamoto ◽  
Katsuhiro Honda ◽  
Akira Notsu ◽  
Hidetomo Ichihashi
Keyword(s):  
Comparative Study ◽  
Iterative Algorithm ◽  
Fuzzy Clustering ◽  
Incomplete Data ◽  
Numerical Experiments ◽  
Missing Values ◽  
Relational Data ◽  
Imputation Methods ◽  
Clustering Model ◽  
Linear Cluster

Relational fuzzy clustering has been developed for extracting intrinsic cluster structures of relational data and was extended to a linear fuzzy clustering model based on Fuzzyc-Medoids (FCMdd) concept, in which Fuzzyc-Means-(FCM-) like iterative algorithm was performed by defining linear cluster prototypes using two representative medoids for each line prototype. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values, and the applicability of several imputation methods is compared. In several numerical experiments, it is demonstrated that some pre-imputation strategies contribute to properly selecting representative medoids of each cluster.

scholarly journals Parallel real-world LU decomposition: Gauss vs. Crout algorithm

2018 ◽  
Vol 8 (1) ◽  
pp. 210-217
Author(s):  
Marek Stabrowski
Keyword(s):  
Real World ◽  
Numerical Experiments ◽  
Matrix Decomposition ◽  
Decomposition Algorithms ◽  
Density Matrices ◽  
Hardware Platform ◽  
Posix Threads ◽  
Real World Applications ◽  
Matrix Decomposition Algorithms ◽  
Algorithm Implementation

Abstract This paper presents numerical experiments with assorted versions of parallel LU matrix decomposition algorithms (Gauss and Crout algorithm). The tests have been carried out on the hardware platform with fourcore Skylake processor featuring hyperthreading technology doubling virtually core number. Parallelization algorithms have been implemented with the aid of classic POSIX threads library. Experiments have shown that basic 4-thread acceleration of all parallel implementations is almost equal to the number of threads/processors. Both algorithms are worth considering in real-world applications (Florida University collection). Gauss algorithm is a better performer, with respect to timing, in the case of matrices with lower density of nonzeros, as opposed to higher density matrices. The latter are processed more efficiently with the aid of Crout algorithm implementation.

Entropy-Regularized Fuzzy Clustering for Non-Euclidean Relational Data and Indefinite Kernel Data

2012 ◽  
Vol 16 (7) ◽  
pp. 784-792 ◽  
Author(s):  
Yuchi Kanzawa ◽  
Keyword(s):  
Fuzzy Clustering ◽  
Numerical Experiments ◽  
Standard Approach ◽  
Relational Data ◽  
Data Types ◽  
Indefinite Kernel ◽  
Clustering Approach ◽  
Theoretical Results

In this paper, an entropy-regularized fuzzy clustering approach for non-Euclidean relational data and indefinite kernel data is developed that has not previously been discussed. It is important because relational data and kernel data are not always Euclidean and positive semi-definite, respectively. It is theoretically determined that an entropy-regularized approach for both non-Euclidean relational data and indefinite kernel data can be applied without using a β-spread transformation, and that two other options make the clustering results crisp for both data types. These results are in contrast to those from the standard approach. Numerical experiments are employed to verify the theoretical results, and the clustering accuracy of three entropy-regularized approaches for non-Euclidean relational data, and three for indefinite kernel data, is compared.

Semi-Supervised Outlier Detection with Only Positive and Unlabeled Data Based on Fuzzy Clustering

2015 ◽  
Vol 24 (03) ◽  
pp. 1550003 ◽  
Author(s):  
Armin Daneshpazhouh ◽  
Ashkan Sami
Keyword(s):  
Intrusion Detection ◽  
Outlier Detection ◽  
Fuzzy Clustering ◽  
Real World ◽  
State Of The Art ◽  
Real Data ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Real World Applications

The task of semi-supervised outlier detection is to find the instances that are exceptional from other data, using some labeled examples. In many applications such as fraud detection and intrusion detection, this issue becomes more important. Most existing techniques are unsupervised. On the other hand, semi-supervised approaches use both negative and positive instances to detect outliers. However, in many real world applications, very few positive labeled examples are available. This paper proposes an innovative approach to address this problem. The proposed method works as follows. First, some reliable negative instances are extracted by a kNN-based algorithm. Afterwards, fuzzy clustering using both negative and positive examples is utilized to detect outliers. Experimental results on real data sets demonstrate that the proposed approach outperforms the previous unsupervised state-of-the-art methods in detecting outliers.

scholarly journals Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering

Algorithms ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 122
Author(s):  
Fevrier Valdez ◽  
Oscar Castillo ◽  
Patricia Melin
Keyword(s):  
Fuzzy Clustering ◽  
Real World ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Metaheuristic Algorithms ◽  
Natural Phenomena ◽  
Algorithm Development ◽  
Real World Applications ◽  
Nature Inspired Algorithms ◽  
Relationship Of

In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problems. These algorithms have been compared with the traditional ones and have demonstrated to be superior in many complex problems. This paper attempts to describe the algorithms based on nature, which are used in optimizing fuzzy clustering in real-world applications. We briefly describe the optimization methods, the most cited ones, nature-inspired algorithms that have been published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new area of nature and bio-inspired optimization of fuzzy clustering.

scholarly journals Electronic Phenomena of Transition Metal Oxides

Crystals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 256
Author(s):  
Christian Rodenbücher ◽  
Kristof Szot
Keyword(s):  
Solid State ◽  
Transition Metal ◽  
Metal Oxides ◽  
Real World ◽  
Transition Metal Oxides ◽  
Major Research ◽  
Research Fields ◽  
Real World Applications

Transition metal oxides with ABO3 or BO2 structures have become one of the major research fields in solid state science, as they exhibit an impressive variety of unusual and exotic phenomena with potential for their exploitation in real-world applications [...]

Sign in / Sign up

Export Citation Format

Share Document