• No results found

[PDF] Top 20 SOMSN: An Effective Self Organizing Map for Clustering of Social Networks

Has 10000 "SOMSN: An Effective Self Organizing Map for Clustering of Social Networks" found on our website. Below are the top 20 most common "SOMSN: An Effective Self Organizing Map for Clustering of Social Networks".

SOMSN: An Effective Self Organizing Map for Clustering of Social Networks

SOMSN: An Effective Self Organizing Map for Clustering of Social Networks

... data, social network analysis has become one of the favorite topics in data ...in social network analysis is to identify communities in the ...network. Clustering is an important task for detection ... See full document

6

Clustering Web Usage Data using Concept Hierarchy and Self Organizing Map

Clustering Web Usage Data using Concept Hierarchy and Self Organizing Map

... of clustering algorithms such as partition based, distance based, density based, grid based, hierarchical and fuzzy clustering algorithms are used to find clusters from Web usage ...data.These ... See full document

7

Increasing the lifetime of wireless sensor networks by Self-Organizing Map algorithm

Increasing the lifetime of wireless sensor networks by Self-Organizing Map algorithm

... Based Clustering Self organizing map ...previous clustering algorithms to energy level of the nodes as a key parameter to cluster formation of the ...topological clustering and ... See full document

8

DEM-based analysis of morphometric features in humid and hyper-arid environments using artificial neural network

DEM-based analysis of morphometric features in humid and hyper-arid environments using artificial neural network

... optimal map is expected to yield the lowest average quantization error, because it is fitted best to the input data (Kohonen, ...optimal map with the lowest average quantization error was selected for ... See full document

12

Proposed Algorithms To Solve Big Data Traveling Salesman Problem

Proposed Algorithms To Solve Big Data Traveling Salesman Problem

... combinatorial clustering as a second proposed algorithm that needs more space and ...Three clustering methods; k-means, GMM, and SOM are tied to select k-means as a faster one and more suitable for big ... See full document

7

Self Organized Mapping based Map Reduce technique in big data analytics: 
		A Neural Nnetwork approach

Self Organized Mapping based Map Reduce technique in big data analytics: A Neural Nnetwork approach

... Hierarchal Self Organizing Map (GHSOM) on Intrusion Detection System (IDS) traces to detect if there are any signature attacks based on topological distances between clustering is used in ... See full document

6

Self-Organizing and Clustering Methods in VANETs

Self-Organizing and Clustering Methods in VANETs

... either self-organising system range (SOSR) or cluster based ...In self organizing system range, If the number of vehicles in a particular area is increasing, and if it causes difficulty for Road Side ... See full document

6

NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map

NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map

... Unlike the previous studies where the weight vectors of dead neurons are far from the input patterns without having any chance to contribute in the learning phase, several different strategies are assigned to the ... See full document

10

Mining hidden information from library databases using SOM (Self Organizing Map)

Mining hidden information from library databases using SOM (Self Organizing Map)

... (Self Organizing Map) on SOM (Self Organizing Map) Algorithm to Order Documents Algorithm to Order Documents Based on Their Full Text ... See full document

22

Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self Organizing Map

Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self Organizing Map

... Besides applications of the algorithm in financial, medical, chemical and biological research (an overview is presented in Kaski, 1997), SOM’s are also used in remote sensing (Richardson et al., 2003; Mercier et al., ... See full document

13

Hybrid Self-Organization Based Facility Layout Planning

Hybrid Self-Organization Based Facility Layout Planning

... layout planning method is proposed in this paper. The method consists of two stages: 1) the automatic self- organized formation of production cells, and 2) expert operator based fine layout planning, which ... See full document

8

Mixed Data Clustering Using Dynamic Growing Hierarchical Self Organizing Map With Improved LM Learning

Mixed Data Clustering Using Dynamic Growing Hierarchical Self Organizing Map With Improved LM Learning

... the map is essential for all the knowledge discovery ...Hierarchical Self- Organizing Map and MLP with Modified LM algorithm is proposed for mixed data ... See full document

7

An Evaluation of Information Technology of Gene
Expression Profiles Processing Stability for Different
Levels of Noise Component

An Evaluation of Information Technology of Gene Expression Profiles Processing Stability for Different Levels of Noise Component

... objective clustering inductive technology involves the division of the initial dataset into two equal power subsets (containing the same quantity of pairwise similar ...the clustering process is carried out ... See full document

15

Application of Self Organizing Map for Exploration of REEs’ Deposition

Application of Self Organizing Map for Exploration of REEs’ Deposition

... groups, clustering methods (unsupervised) are ...data, clustering algorithms would be useful for recog- nition of elements ...paper, Self-Organizing Map (SOM) algorithm, as an unsu- ... See full document

12

Incipient Faults Diagnosis by Employing Self Organizing Map

Incipient Faults Diagnosis by Employing Self Organizing Map

... Where, - Number of clusters, - average distance of all objects from the cluster to their cluster centre, - distance between clusters centres. Hence the ratio is small if the clusters are compact and far from each other. ... See full document

11

Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks

Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks

... In conclusion, the best MLP model developed in this study has a higher rate of detection of mastitis than the previously reported models for AMS. Furthermore, the SOM model could be used to identify the stage of pro- ... See full document

8

A REVIEW ON SELF-ORGANIZING CLUSTERING METHODS FOR ENERGY-EFFICIENT DATA GATHERING IN SENSOR NETWORKS

A REVIEW ON SELF-ORGANIZING CLUSTERING METHODS FOR ENERGY-EFFICIENT DATA GATHERING IN SENSOR NETWORKS

... Distance between CHs and the BS to ensure that CHs are not too far from the BS, making inter-cluster communication or communication between CH and BS expensive Uniform CH distribution so that CHs are not cluttered. ... See full document

8

Radial Basis Function based Self-Organizing Map Model for Clustering Spatial Data using PCA

Radial Basis Function based Self-Organizing Map Model for Clustering Spatial Data using PCA

... The other problem can be solved by making the number of attributes smaller in size. This should be done in such a way that the selected attributes can capture the essential information in data i.e. minor information will ... See full document

7

Effective Character Recognition using ANN & Convolution Techniques.

Effective Character Recognition using ANN & Convolution Techniques.

... There are three basic steps involved in the application of the algorithm after initialization: sampling, similarity matching, and updating. These three steps are repeated until formation of the feature map has ... See full document

5

Application of Data Mining in predicting a Course for a Student Based on Previous Records, Financial Status and Personality Traits

Application of Data Mining in predicting a Course for a Student Based on Previous Records, Financial Status and Personality Traits

... Clustering aims to partition n observations into k clusters. Each observation belongs to the cluster with the nearest mean. The following Figure 1 shows the implementation of K-means clustering using GMDH ... See full document

5

Show all 10000 documents...