[PDF] Top 20 NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map
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NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map
... of data analysis. This paper proposes a novel data clustering algorithm to increase the clustering ...A novel game theoretic self-organizing ... See full document
10
A Novel Clustering Algorithm for DEEC Protocol Based on Game Theory in WSN Sensors
... Efficient Clustering Scheme for Self-Organizing Distributed Wireless Sensor Networks (EECS) ...gathers data intermittently from a landscape where every hub constantly faculties nature and ... See full document
5
Clustering Web Usage Data using Concept Hierarchy and Self Organizing Map
... Various data mining methods have been have been used to generate models of usage ...Models based on association rules, clustering algorithms, sequential analysis and Markov models have been used for ... See full document
7
Increasing the lifetime of wireless sensor networks by Self-Organizing Map algorithm
... and clustering of ...for clustering and their analysis to study unpredictable behaviors of network parameters and ...Kohonen Self Organizing Map (KSOM) is computed for various numbers ... See full document
8
The ubiquitous self-organizing map for non-stationary data streams
... UbiSOM algorithm generally converges faster to stationary phases of the distribu- tions, while the PLSOM converges less steadily and slower in half of the streams, and the convergence of the Online SOM is dictated ... See full document
22
Firefly Algorithm based Map Reduce for Large Scale Data Clustering
... produced. These parameters are assumed as the solution for the engaged FF. One of the metrics that is used to estimate the quality of the cluster and fitness of the solution is sum of the squared Euclidean distance. The ... See full document
7
Clustering based information retrieval with the aco and the k-means clustering algorithm
... for clustering may have been written by different groups, from different viewpoints, or have different writing style, clustering these textual materials is, therefore, a challenge due to the diversity of ... See full document
6
FPGA-Based Acceleration of the Self-Organizing Map (SOM) Algorithm using High-Level Synthesis
... FPGAs are programmable logic devices that provides greater flexibility and high throughput. The designs implemented in FPGA are mainly done using HDLs such as Verilog and VHSIC Hardware Description Language (VHDL) ... See full document
110
Self-organizin map clustering method for the analysis of e-learning activities
... Factors that affect students‘ interactions with e-learning include their learning behaviors. The learning behaviors of students are defined by meaningful learning characteristics. The meaningful learning characteristics ... See full document
27
Mixed Data Clustering Using Dynamic Growing Hierarchical Self Organizing Map With Improved LM Learning
... GHSOM can directly handle numeric, categorical and mixed data. EAOI can be used to investigate major values and it resolves the problems with discretizing numeric attributes. Although the structure of GHSOM is ... See full document
7
An Application of Partitive Clustering Algorithm for Landslide Hazard Zonation
... can map the areas and can be used as tools to visualize the identified causative factors of the ...partitive clustering algorithms differs since K-Means is sensitive to ...trained map before starting ... See full document
6
A Self Learning Diagnosis Algorithm Based on Data Clustering
... a self-learning diagnostic algorithm. The self-learn- ing algorithm creates models of the object under ...the algorithm collects data received from sensors. Then the ... See full document
9
A Comparison among Data Mining Algorithms for Outlier Detection using Flow Pattern Experiments
... input data into a xed number of nodes. They learn to cluster data based on similarity, topology, with a preference (but no guarantee) for assigning the same number of instances to each ...preserving ... See full document
16
Big Data Analytics: Map Reduce Function using BIRCH Clustering Algorithm
... Start with the root, Find the CF entry in the root closest to the data point, move to that child and repeat the process until the closest leaf entry is found. At the leaf, If the point can be accommodated in the ... See full document
8
Social Interaction and Self-Organizing Map
... Abstract– Self Organizing Maps or SOMs are mostly used to represent a multidimensional data in much lower dimension ...dimensions, Clustering, Exploratory data analysis and ... See full document
8
A Review on Clustering Analysis based on Optimization Algorithm for Datamining
... – Clustering analysis is one of the important concept of data ...the clustering problem it is one of the research based ...The clustering is belongs to the unsupervised learning in ... See full document
6
Effective Character Recognition using ANN & Convolution Techniques.
... a self-organizing map, the neurons are placed at the nodes of a lattice that is usually one or two- ....A self-organizing map is therefore characterized by the formation of a ... See full document
5
IMPACT: A Novel Clustering Algorithm based on Attraction
... Grid-based clustering algorithms limit the search space into segments ...the data into sub- space regions ...attraction based data clustering algorithm. The input ... See full document
13
An Attempt to Recognize Handwritten Tamil Character Using Kohonen SOM
... Text area from the document, which may consist of multi lines, is extracted and the segmentation step is followed. Further, each line is segmented into individual words, and finally ach word is segmented into individual ... See full document
5
Semi-supervised consensus clustering for gene expression data analysis
... consensus clustering method, designed an algorithm, and compared it with another semi-supervised clustering algorithm, a consensus clustering algorithm and a simple ... See full document
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