scholarly journals NODE DETECTION USING HIGH-DIMENSIONAL FUZZY PARCELLATION APPLIED TO THE INSULAR CORTEX

2014 ◽  
Author(s):  
Ugo Vercelli ◽  
Matteo Diano ◽  
Tommaso Costa ◽  
Sergio Duca ◽  
Giuliano Geminiani ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Clustering Algorithm ◽  
Insular Cortex ◽  
Estimation Procedure ◽  
High Dimensional ◽  
Border Detection ◽  
Network Nodes ◽  
Region Model ◽  
One Step ◽  
Definition Of

Several functional connectivity approaches require the definition of a set of ROIs that act as network nodes. Different methods have been developed to define these nodes and to derive their functional and effective connections, most of which are rather complex. Here we aim to propose a relatively simple “one-step” border detection and ROI estimation procedure employing the fuzzy c-mean clustering algorithm. To test this procedure and to explore insular connectivity beyond the two/three-region model currently proposed in the literature, we parcellated the insular cortex of a group of twenty healthy right-handed volunteers (10 females) scanned in a resting state condition. Employing a high-dimensional functional connectivity-based clustering process, we confirmed the two patterns of connectivity previously described. This method revealed a complex pattern of functional connectivity where the two previously detected insular clusters are subdivided into several other networks, some of which not commonly associated with the insular cortex, such as the default mode network and parts of the dorsal attentional network. Finally, the detection of nodes was reliable

scholarly journals Node Detection Using High-Dimensional Fuzzy Parcellation Applied to the Insular Cortex

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Ugo Vercelli ◽  
Matteo Diano ◽  
Tommaso Costa ◽  
Andrea Nani ◽  
Sergio Duca ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Clustering Algorithm ◽  
Insular Cortex ◽  
Estimation Procedure ◽  
High Dimensional ◽  
Border Detection ◽  
Network Nodes ◽  
Replication Group ◽  
Region Model ◽  
Definition Of

Several functional connectivity approaches require the definition of a set of regions of interest (ROIs) that act as network nodes. Different methods have been developed to define these nodes and to derive their functional and effective connections, most of which are rather complex. Here we aim to propose a relatively simple “one-step” border detection and ROI estimation procedure employing the fuzzyc-mean clustering algorithm. To test this procedure and to explore insular connectivity beyond the two/three-region model currently proposed in the literature, we parcellated the insular cortex of 20 healthy right-handed volunteers scanned in a resting state. By employing a high-dimensional functional connectivity-based clustering process, we confirmed the two patterns of connectivity previously described. This method revealed a complex pattern of functional connectivity where the two previously detected insular clusters are subdivided into several other networks, some of which are not commonly associated with the insular cortex, such as the default mode network and parts of the dorsal attentional network. Furthermore, the detection of nodes was reliable, as demonstrated by the confirmative analysis performed on a replication group of subjects.

Towards Open Graphical Tool-Building Framework

2011 ◽  
Vol 43 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Edgars Rencis ◽  
Janis Barzdins ◽  
Sergejs Kozlovics
Keyword(s):  
Deep Understanding ◽  
Easy Task ◽  
Domain Specific ◽  
Activity Diagrams ◽  
Graphical Tool ◽  
One Step ◽  
Uml Activity Diagrams ◽  
Definition Of ◽  
Tool Building

Towards Open Graphical Tool-Building Framework Nowadays, there are many frameworks for developing domain-specific tools. However, if we want to create a really sophisticated tool with specific functionality requirements, it is not always an easy task to do. Although tool-building platforms offer some means for extending the tool functionality and accessing it from external applications, it usually requires a deep understanding of various technical implementation details. In this paper we try to go one step closer to a really open graphical tool-building framework that would allow both to change the behavior of the tool and to access the tool from the outside easily. We start by defining a specialization of metamodels which is a great and powerful facility itself. Then we go on and show how this can be applied in the field of graphical domain-specific tool building. The approach is demonstrated on an example of a subset of UML activity diagrams. The benefits of the approach are also clearly indicated. These include a natural and intuitive definition of tools, a strict logic/presentation separation and the openness for extensions as well as for external applications.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals A Summary of F-Transform Techniques in Data Analysis

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1771
Author(s):  
Ferdinando Di Martino ◽  
Irina Perfilieva ◽  
Salvatore Sessa
Keyword(s):  
Data Analysis ◽  
Analysis Data ◽  
High Dimensional ◽  
Sufficient Data ◽  
Fuzzy Transform ◽  
Transform Method ◽  
Data Density ◽  
Fuzzy Partitions ◽  
Definition Of ◽  
Performance Results

Fuzzy transform is a technique applied to approximate a function of one or more variables applied by researchers in various image and data analysis. In this work we present a summary of a fuzzy transform method proposed in recent years in different data mining disciplines, such as the detection of relationships between features and the extraction of association rules, time series analysis, data classification. After having given the definition of the concept of Fuzzy Transform in one or more dimensions in which the constraint of sufficient data density with respect to fuzzy partitions is also explored, the data analysis approaches recently proposed in the literature based on the use of the Fuzzy Transform are analyzed. In particular, the strategies adopted in these approaches for managing the constraint of sufficient data density and the performance results obtained, compared with those measured by adopting other methods in the literature, are explored. The last section is dedicated to final considerations and future scenarios for using the Fuzzy Transform for the analysis of massive and high-dimensional data.

Border Detection in Skin Lesion Images Using an Improved Clustering Algorithm

2020 ◽  
Vol 16 (4) ◽  
pp. 15-29
Author(s):  
Jayalakshmi D. ◽  
Dheeba J.
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Clustering Algorithm ◽  
Median Filter ◽  
Second Phase ◽  
Border Detection ◽  
Early Computer ◽  
Segmentation Methods ◽  
Two Phases ◽  
Skin Cancer Detection

The incidence of skin cancer has been increasing in recent years and it can become dangerous if not detected early. Computer-aided diagnosis systems can help the dermatologists in assisting with skin cancer detection by examining the features more critically. In this article, a detailed review of pre-processing and segmentation methods is done on skin lesion images by investigating existing and prevalent segmentation methods for the diagnosis of skin cancer. The pre-processing stage is divided into two phases, in the first phase, a median filter is used to remove the artifact; and in the second phase, an improved K-means clustering with outlier removal (KMOR) algorithm is suggested. The proposed method was tested in a publicly available Danderm database. The improved cluster-based algorithm gives an accuracy of 92.8% with a sensitivity of 93% and specificity of 90% with an AUC value of 0.90435. From the experimental results, it is evident that the clustering algorithm has performed well in detecting the border of the lesion and is suitable for pre-processing dermoscopic images.

Research on Spectral Clustering

2014 ◽  
Vol 687-691 ◽  
pp. 1350-1353
Author(s):  
Li Li Fu ◽  
Yong Li Liu ◽  
Li Jing Hao
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Theoretical Foundation ◽  
Clustering Algorithms ◽  
Spectral Graph Theory ◽  
Graph Partition ◽  
Mining Areas ◽  
Spectral Graph ◽  
Definition Of ◽  
Spectral Clustering Algorithm

Spectral clustering algorithm is a kind of clustering algorithm based on spectral graph theory. As spectral clustering has deep theoretical foundation as well as the advantage in dealing with non-convex distribution, it has received much attention in machine learning and data mining areas. The algorithm is easy to implement, and outperforms traditional clustering algorithms such as K-means algorithm. This paper aims to give some intuitions on spectral clustering. We describe different graph partition criteria, the definition of spectral clustering, and clustering steps, etc. Finally, in order to solve the disadvantage of spectral clustering, some improvements are introduced briefly.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

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