Clustering Analysis of Competitive Learning Network for Molecular Data

2006 ◽  
pp. 1244-1249 ◽  
Author(s):  
Lin Wang ◽  
Minghu Jiang ◽  
Yinghua Lu ◽  
Frank Noe ◽  
Jeremy C. Smith
Keyword(s):  
Clustering Analysis ◽  
Molecular Data ◽  
Competitive Learning ◽  
Learning Network

A COMPARATIVE STUDY OF CLUSTERING METHODS FOR MOLECULAR DATA

2007 ◽  
Vol 17 (06) ◽  
pp. 447-458 ◽  
Author(s):  
LIN WANG ◽  
MINGHU JIANG ◽  
YINGHUA LU ◽  
MINFU SUN ◽  
FRANK NOE
Keyword(s):  
Hierarchical Cluster ◽  
Molecular Data ◽  
Competitive Learning ◽  
Clustering Methods ◽  
Learning Network ◽  
Large Size ◽  
Cluster Distance ◽  
Standard Deviation Range ◽  
Multi Level ◽  
Distance Relation

The research aim is to use three clustering technologies for establishing molecular data model of large size sets by comparison between low energy samples (LES) and local molecular samples (LMS). Hierarchical cluster of multi-level tree distance relation, competitive learning network of similar inputs falling into the same cluster and topological SOM are used to analyze 6242 LES and 5000 LMS. Our experiments show that in SOM, there are 24 to 25 Davies-Boulding clustering index and color map cluster units in the LES more than 10 to 12 in the LMS, which is consistent with the results of hierarchical cluster and competitive learning network in the rough. The hierarchical cluster reflects the biggest inter-cluster distance about 30 for the LES is far larger than that of LMS about 10. The intra-cluster distance of LES about 15 is also far bigger than that of LMS about 3. In SOM, there are more cluster borders of high values (black) reflecting large distance and more clusters in the D-matrix and U-matrix of LES than that of LMS, due to the biggest standard deviation range from -8 to 10 of samples feature of the LES is bigger than that of LMS from -2.5 to 2.5.

scholarly journals Hierarchical clustering analysis of pathologic and molecular data identifies prognostically and biologically distinct groups of colorectal carcinomas

2010 ◽  
Vol 24 (1) ◽  
pp. 126-137 ◽  
Author(s):  
Daniela Furlan ◽  
Ileana W Carnevali ◽  
Barbara Bernasconi ◽  
Nora Sahnane ◽  
Katia Milani ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
Molecular Data ◽  
Hierarchical Clustering Analysis ◽  
Colorectal Carcinomas

Phylogenetic relationship of some Onobrychis taxa naturally grown in Turkey based on morphology and nuclear ribosomal DNA ITS Sequences

2016 ◽  
Author(s):  
Süleyman AVCI ◽  
Nilgün Tekin ◽  
Cengiz SANCAK ◽  
Sebahattin ÖZCAN ◽  
Amir Orojpour Marangi
Keyword(s):  
Ribosomal Dna ◽  
Clustering Analysis ◽  
Molecular Data ◽  
Its Sequences ◽  
Nuclear Ribosomal Dna ◽  
Morphological Data ◽  
Nrdna Its ◽  
Neighbor Joining ◽  
Quantitative Characters ◽  
Relationship Of

Phylogenetic relationships were studied based on morphology and nuclear ribosomal DNA internal transcript spacer (nrDNA ITS) sequences in 49 populations of 36 Onobrychis taxa belonging to 5 sections (Dendobrychis, Lophobrychis, Onobrychis, Heliobrychis and Hymenobrychis) naturally grown in Turkey. The morphological data (46 quantitative characters) were standardized and submitted for PCA analysis before the performance of hierarchical clustering analysis. In addition, a 577-bp region of the nrDNA ITS sequences was compared to generate a dendrogram using the neighbor-joining method. According to the obtained results, the Onobrychis genus is clearly divided into the Onobrychis and Sisyrosema subgenera both molecularly and morphologically; however, in terms of molecular data, while the Onobrychis subgenus showed polyphyletic features, the Sisyrosema subgenus had monophyletic features. Additionally, long-wing taxa belonging to Dendobrychis and Onobrychis and O. crista-galli belonging to Lophobrychis located in the same clade in Onobrychis showed polyphyletic features. This finding was confirmed in terms of long-wing taxa as well as morphology and indicated that these three sections were entangled with each other under the Onobrychis subgenus.

Self-Organizing Map Clustering Analysis for Molecular Data

2006 ◽  
pp. 1250-1255 ◽  
Author(s):  
Lin Wang ◽  
Minghu Jiang ◽  
Yinghua Lu ◽  
Frank Noe ◽  
Jeremy C. Smith
Keyword(s):  
Clustering Analysis ◽  
Molecular Data ◽  
Self Organizing Map ◽  
Self Organizing

scholarly journals Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network

Algorithms ◽  
2014 ◽  
Vol 7 (4) ◽  
pp. 492-509 ◽  
Author(s):  
Xuebin Qin ◽  
Mei Wang ◽  
Jzau-Sheng Lin ◽  
Xiaowei Li
Keyword(s):  
Competitive Learning ◽  
Power Cable ◽  
Learning Network ◽  
Fault Recognition

A methodology to carry out voting classification tasks using a particle swarm optimization-based neuro-fuzzy competitive learning network

2016 ◽  
Vol 8 (1) ◽  
pp. 49-69 ◽  
Author(s):  
Androniki Tamvakis ◽  
George E. Tsekouras ◽  
Anastasios Rigos ◽  
Christos Kalloniatis ◽  
Christos-Nikolaos Anagnostopoulos ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Competitive Learning ◽  
Swarm Optimization ◽  
Learning Network ◽  
Neuro Fuzzy ◽  
Classification Tasks
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