scholarly journals Fuzzy Covering-Based Three-Way Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Dandan Yang
Keyword(s):  
Nearest Neighbor ◽  
Structural Information ◽  
Boundary Region ◽  
Global Perspective ◽  
Semantic Interpretation ◽  
Entropy Theory ◽  
Fuzzy Similarity ◽  
Fuzzy Similarity Relation ◽  
The Universe ◽  
Better Than

This paper investigates the three-way clustering involving fuzzy covering, thresholds acquisition, and boundary region processing. First of all, a valid fuzzy covering of the universe is constructed on the basis of an appropriate fuzzy similarity relation, which helps capture the structural information and the internal connections of the dataset from the global perspective. Due to the advantages of valid fuzzy covering, we explore the valid fuzzy covering instead of the raw dataset for RFCM algorithm-based three-way clustering. Subsequently, from the perspective of semantic interpretation of balancing the uncertainty changes in fuzzy sets, a method of partition thresholds acquisition combining linear and nonlinear fuzzy entropy theory is proposed. Furthermore, boundary regions in three-way clustering correspond to the abstaining decisions and generate uncertain rules. In order to improve the classification accuracy, the k-nearest neighbor (kNN) algorithm is utilized to reduce the objects in the boundary regions. The experimental results show that the performance of the proposed three-way clustering based on fuzzy covering and kNN-FRFCM algorithm is better than the compared algorithms in most cases.

GENERALIZED FUZZY ROUGH SETS BY CONDITIONAL PROBABILITY RELATIONS

2002 ◽  
Vol 16 (07) ◽  
pp. 865-881 ◽  
Author(s):  
ROLLY INTAN ◽  
MASAO MUKAIDONO
Keyword(s):  
Conditional Probability ◽  
Rough Set ◽  
Real World ◽  
Rough Sets ◽  
Similarity Relation ◽  
Fuzzy Rough Set ◽  
Fuzzy Similarity ◽  
Fuzzy Similarity Relation ◽  
Real World Applications ◽  
The Universe

In 1982, Pawlak proposed the concept of rough sets with a practical purpose of representing indiscernibility of elements or objects in the presence of information systems. Even if it is easy to analyze, the rough set theory built on a partition induced by equivalence relation may not provide a realistic view of relationships between elements in real-world applications. Here, coverings of, or nonequivalence relations on, the universe can be considered to represent a more realistic model instead of a partition in which a generalized model of rough sets was proposed. In this paper, first a weak fuzzy similarity relation is introduced as a more realistic relation in representing the relationship between two elements of data in real-world applications. Fuzzy conditional probability relation is considered as a concrete example of the weak fuzzy similarity relation. Coverings of the universe is provided by fuzzy conditional probability relations. Generalized concepts of rough approximations and rough membership functions are proposed and defined based on coverings of the universe. Such generalization is considered as a kind of fuzzy rough set. A more generalized fuzzy rough set approximation of a given fuzzy set is proposed and discussed as an alternative to provide interval-value fuzzy sets. Their properties are examined.

scholarly journals Fuzzy Knowledge Distance with Three-Layer Perspectives in Neighborhood System

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jie Yang ◽  
Tian Luo ◽  
Fan Zhao ◽  
Shuai Li ◽  
Wei Zhou
Keyword(s):  
Rough Sets ◽  
Structural Information ◽  
Boundary Region ◽  
Basic Element ◽  
Target Concept ◽  
Information Granule ◽  
Density Peaks ◽  
Neighborhood System ◽  
Neighborhood Rough Sets ◽  
The Difference

Information granule is the basic element in granular computing (GrC), and it can be obtained according to the granulation criterion. In neighborhood rough sets, current uncertainty measures focus on computing the knowledge granulation of single granular space and have two main limitations: (i) neglecting the structural information of boundary regions and (ii) the inability to reflect the difference between neighborhood granular spaces with the same uncertainty for approximating a target concept. Firstly, a fuzziness-based uncertainty measure for neighborhood rough sets is introduced to characterize the structural information of boundary regions. Moreover, from the perspective of distance, based on the idea of density peaks, we present a fuzzy-neighborhood-granule-distance- (FNGD-) based method to discover the relationship between granules in a granular space. Then, to characterize the difference between granular spaces for approximating a target concept, we present the fuzzy neighborhood granular space distance (FNGSD) and fuzzy neighborhood boundary region distance (FNBRD). FNGD, FNGSD, and FNBRD are hierarchically organized from fineness to coarseness according to the semantics of granularity, which provide three-layer perspectives in the neighborhood system.

Image Completion Using Randomized Correspondence

2011 ◽  
Vol 271-273 ◽  
pp. 229-234
Author(s):  
Yun Ling ◽  
Hai Tao Sun ◽  
Jian Wei Han ◽  
Xun Wang
Keyword(s):  
Real Time ◽  
Random Sampling ◽  
Nearest Neighbor ◽  
Structural Constraint ◽  
Image Completion ◽  
Image Patches ◽  
Real Time Applications ◽  
Surrounding Areas ◽  
Straight Lines ◽  
Better Than

Image completion techniques can be used to repair unknown image regions. However, existing techniques are too slow for real-time applications. In this paper, an image completion technique based on randomized correspondence is presented to accelerate the completing process. Some good patch matches are found via random sampling and propagated to surrounding areas. Approximate nearest neighbor matches between image patches can be found in real-time. For images with strong structure, straight lines or curves across unknown regions can be manually specified to preserve the important structures. In such case, search is only performed on specified lines or curves. Finally, the remaining unknown regions can be filled using randomized correspondence with structural constraint. The experiments show that the quality and speed of presented technique are much better than that of existing methods.

Web Graph Clustering for Displays and Navigation of Cyberspace

Web Mining ◽  
2011 ◽  
pp. 253-275
Author(s):  
Xiaodi Huang ◽  
Wei Lai
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Structural Information ◽  
Graph Clustering ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
New Approach ◽  
Web Graph

This chapter presents a new approach to clustering graphs, and applies it to Web graph display and navigation. The proposed approach takes advantage of the linkage patterns of graphs, and utilizes an affinity function in conjunction with the k-nearest neighbor. This chapter uses Web graph clustering as an illustrative example, and offers a potentially more applicable method to mine structural information from data sets, with the hope of informing readers of another aspect of data mining and its applications.

Uncovering community structure in networks via hybrid clustering using cascading failure dynamics and topological metric functions

2019 ◽  
Vol 33 (29) ◽  
pp. 1950352
Author(s):  
Bo Yang ◽  
Tao Huang ◽  
Xu Li
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Nearest Neighbor ◽  
Structural Information ◽  
Point Of View ◽  
Cascading Failures ◽  
Hybrid Clustering ◽  
Mesoscale Structure ◽  
Metric Functions ◽  
Global And Local

Many networks have community structure — groups of nodes within which connections are dense but between which they are sparser. While there exists a range of algorithms for community detection in networks, most of them try to discover this important mesoscale structure from a topological point of view solely. Here we develop a hybrid clustering approach for uncovering the community structure in a network using a combination of information on local topology of the network and on the dynamics of the cascading failures. The originality of the proposed approach is that we introduce a novel fusion of the dynamic behaviors of the cascading failures and topological metric functions in the [Formula: see text]th-nearest neighbor density scheme, which integrates both the global and local structural information of a given network for community detection. The experimental results on both artificial random and real-world benchmark networks indicate the effectiveness and reliability of our approach.

The NCEM One-Angstrom Microscope Project Reaches 0.89Å Resolution

2000 ◽  
Vol 6 (S2) ◽  
pp. 1192-1193 ◽  
Author(s):  
Michael A. O'Keefe
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Image Reconstruction ◽  
Structural Information ◽  
Computer Software ◽  
Image Simulation ◽  
Resolution Limit ◽  
Transmission Electron ◽  
Better Than

Transmission electron microscopy to a resolution of 0.89Å has been achieved at the National Center for Electron Microscopy and is available to electron microscopists who have a requirement for this level of resolution. Development of this capability commenced in 1993, when the National Center for Electron Microscopy agreed to fund a proposal for a unique facility, a one- Ångstrom microscope (OÅM).2 The OÅM project provides materials scientists with transmission electron microscopy at a resolution better than one Angstrom by exploiting the significantly higher information limit of a FEG-TEM over its Scherzer resolution limit. To turn the misphased information beyond the Scherzer limit into useful resolution, the OÅM requires extensive image reconstruction. One method chosen was reconstruction from off-axis holograms; another was reconstruction from focal series of underfocused images. The OÅM is then properly a combination of a FEG-TEM (a CM300FEG-UT) together with computer software able to generate sub-Ångstrom images from experimental images obtained on the FEG-TEM.Before the advent of the OÅM, NCEM microscopists relied on image simulation to obtain structural information beyond the TEM resolution limit.

Improving the Performance of an Associative Classifier by Gamma Rough Sets Based Instance Selection

2017 ◽  
Vol 32 (01) ◽  
pp. 1860009 ◽  
Author(s):  
Jarvin A. Antón-Vargas ◽  
Yenny Villuendas-Rey ◽  
Cornelio Yáñez-Márquez ◽  
Itzamá López-Yáñez ◽  
Oscar Camacho-Nieto
Keyword(s):  
Information Systems ◽  
Rough Sets ◽  
Nearest Neighbor ◽  
Management Information Systems ◽  
Computational Cost ◽  
Instance Selection ◽  
Management Information ◽  
Similarity Relations ◽  
The Universe ◽  
Selection Of

This paper introduces the Gamma Rough Sets for management information systems where the universe objects are represented by continuous attributes and are connected by similarity relations. Some properties of such sets are demonstrated in this paper. In addition, Gamma Rough Sets are used to improve the Gamma associative classifier, by selecting instances. The results indicate that the selection of instances significantly reduces the computational cost of the Gamma classifier without affecting its effectiveness. The results also suggest that the selection of instances using Gamma Rough Sets favors other lazy learners, such as Nearest Neighbor and ALVOT.

scholarly journals Gait Recognition Using GEI and AFDEI

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Jing Luo ◽  
Jianliang Zhang ◽  
Chunyuan Zi ◽  
Ying Niu ◽  
Huixin Tian ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Gait Recognition ◽  
Dynamic Information ◽  
Static Information ◽  
Gait Feature ◽  
Time Requirements ◽  
The Common ◽  
The Moment ◽  
Neighbor Classifier ◽  
Better Than

Gait energy image (GEI) preserves the dynamic and static information of a gait sequence. The common static information includes the appearance and shape of the human body and the dynamic information includes the variation of frequency and phase. However, there is no consideration of the time that normalizes each silhouette within the GEI. As regards this problem, this paper proposed the accumulated frame difference energy image (AFDEI), which can reflect the time characteristics. The fusion of the moment invariants extracted from GEI and AFDEI was selected as the gait feature. Then, gait recognition was accomplished using the nearest neighbor classifier based on the Euclidean distance. Finally, to verify the performance, the proposed algorithm was compared with the GEI + 2D-PCA and SFDEI + HMM on the CASIA-B gait database. The experimental results have shown that the proposed algorithm performs better than GEI + 2D-PCA and SFDEI + HMM and meets the real-time requirements.

scholarly journals The insulin A and B chains contain structural information for the formation of the native molecule. Studies with protein disulphide-isomerase

1990 ◽  
Vol 268 (2) ◽  
pp. 429-435 ◽  
Author(s):  
J G Tang ◽  
C L Tsou
Keyword(s):  
Structural Information ◽  
Sufficient Information ◽  
Appreciable Effect ◽  
Dilute Solution ◽  
Reaction Products ◽  
C Peptide ◽  
Native Insulin ◽  
Predominant Product ◽  
Better Than

It has been shown previously [Tang, Wang & Tsou (1988) Biochem. J. 255, 451-455] that, under appropriate conditions, native insulin can be obtained from scrambled insulin or the S-sulphonates of the chains with a yield of 25-30%, together with reaction products containing the separated A and B chains. The native hormone is by far the predominant product among the isomers containing both chains. It is now shown that the presence of added C peptide has no appreciable effect on the yield of native insulin. At higher temperatures the content of the native hormone decreases whereas those of the separated chains increase, and in no case was scrambled insulin containing both chains the predominant product in the absence of denaturants. Both the scrambling and the unscrambling reactions give similar h.p.l.c. profiles for the products. Under similar conditions cross-linked insulin with native disulphide linkages can be obtained from the scrambled molecule or from the S-sulphonate derivative with yields of 50% and 75% respectively at 4 degrees C, and with a dilute solution of the hexa-S-sulphonate yields better than 90% can be obtained. The regenerated product is shown to have the native disulphide bridges by treatment with CNBr to give insulin and by the identity of the h.p.l.c. profile of its peptic hydrolysate with that for cross-linked insulin. It appears that the insulin A and B chains contain sufficient information for the formation of the native molecule and that the role of the connecting C peptide is to bring and to keep the two chains together.

Approximate Data Querying in Fuzzy Relational Database

2002 ◽  
Vol 6 (1) ◽  
pp. 33-40
Author(s):  
Rolly Intan ◽  
◽  
Masao Mukaidono ◽  
Keyword(s):  
Relational Database ◽  
Similarity Relation ◽  
Imprecise Data ◽  
Fuzzy Information ◽  
Fuzzy Similarity ◽  
Fuzzy Similarity Relation ◽  
Data Querying ◽  
Fuzzy Query ◽  
Probability Relation ◽  
Query System

Fuzzy relational database was proposed for dealing with imprecise data or fuzzy information in a relational database. In order to provide a more realistic relation in representing similarity between two imprecise data, we need to weaken fuzzy similarity relation to be weak fuzzy similarity relation in which fuzzy conditional probability relation (FCPR, for short) is regarded as a concrete example of the weak fuzzy similarity relation. In this paper, application of approximate data querying is discussed induced by FCPR in the presence of the fuzzy relational database. Application of approximate data querying in order to provide fuzzy query relation is presented into two frameworks, namely dependent inputs and independent inputs. Finally, related to join operator, approximate join of two or more fuzzy query relations is given for the purpose of extending query system.

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