self-organizing clustering algorithm
Energy Based Clustering Self Organizing Map Protocol For Wireless Sensor Networks
... The proposed algorithm is more like LEACH-C and LEA2C protocols. Thus the assumption about BS cluster formation tasks and energy consumptions models of normal and cluster head nodes are the same as previous. The ...
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SELF ORGANIZING MAP BASED CLUSTERING MODEL BY ANALYZING EIGEN SYSTEM OF PCA
... It has already been discussed in previous section that PCA and SOM both are capable of retaining the essential information of a dataset in lesser number of selected components individually in a sophisticated way.Number ...
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NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map
... data clustering algorithm is proposed by combining a new initialization method, Game theory, and SOM ...existing clustering techniques where the weight vectors of non-winning neurons are far from the ...
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Self-organizin map clustering method for the analysis of e-learning activities
... of Self Organizing Maps (SOM) algorithm in grouping e-learning students into clusters of students who have similar ways of carrying out their activities in e-learning environment, and to identify the ...
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Catchment classification by runoff behaviour with self organizing maps (SOM)
... In hydrology, a classification of catchments based on a rig- orous analysis of patterns in observed data is almost non- existent (McDonnell and Woods, 2004; Woods, 2002). Clas- sification is a task of learning a ...
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INTERACTING THROUGH DISCLOSING: PEER INTERACTION PATTERNS BASED ON SELF DISCLOSURE LEVELS VIA FACEBOOK
... the Self-Organizing Map algorithm, a testing data set and a training data set with the multi- dimensional vectors based on the sentiment lexicons of the bESD with Hadoop Map (M) /Reduce (R) in the ...
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Clustering Algorithm of Quantum Self Organization Network
... unsupervised self-organizing neural network is rela- tively ...classical clustering algorithms, Cai et al. [10] proposed a new algorithm called K-Distributions for Clustering ...
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Data Clustering and Topology Preservation Using 3D Visualization of Self Organizing Maps
... of clustering data in 3D xyz-axis instead of data clustering in traditional 2D-SOM xy-axis output ...technique, algorithm, output neuron arrangements and also C/C++ ...SOM algorithm as ...
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Student behavior analysis using self organizing map clustering technique
... used. Self-organizing map (SOM) [7] is chosen as a mining technique in order to analyze and cluster the students’ behavior that have been captured from the log data ...network algorithm to perform ...
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Semi-supervised consensus clustering for gene expression data analysis
... final clustering. The consensus clustering algorithms differ in chosen algo- rithms for basic clustering, consensus function and final ...hierarchical clustering(HC) or ...
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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 ...
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Title: WEB PAGE CLUSTERING USING SELF-ORGANIZING MAP
... The first step in constructing a SOM is to initialize the weight vectors. From there the algorithm selects a sample vector randomly and searches the map of weight vectors to find the weight that can represent the ...
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A modified kohonen self-organizing map (KSOM) clustering for four categorical data
... KSOM algorithm has succeeded in clustering and solving complex problems in many areas, especially when they involve high dimensional ...KSOM algorithm is incapable of handling the feature similarity ...
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SOMSN: An Effective Self Organizing Map for Clustering of Social Networks
... of clustering algorithms have been proposed for data classification based on centrality ...an algorithm for this purpose which has become one of the well-known algorithms in this regard ...another ...
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Kohonen Self Organizing Map with Modified K-means clustering For High Dimensional Data Set
... nonhierarchical clustering methods is the ...K-means algorithm searches for a partition of X into k clusters that minimizes the within groups sum of squared ...K-means algorithm starts by ...
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Application of Data Mining in predicting a Course for a Student Based on Previous Records, Financial Status and Personality Traits
... earlier, clustering is used in order to obtain useful knowledge from the ...class. Clustering is the process of making a group of abstract objects into classes of similar ...objects. Clustering is ...
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Increasing the lifetime of wireless sensor networks by Self-Organizing Map algorithm
... phase clustering method SOM followed by K-means algorithm which had been proposed in with an exact comparison between the results of direct clustering of data and clustering of the prototype ...
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Neural Network based Clustering using Visual Features of Characters‟ Shape in Image
... counts Self-organizing Neural Network for identifying consumer lifestyles which is an important characteristic and individual purchasing behavior of the consumer in the ...the algorithm. In his paper ...
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A Self-organizing and Self-configuration Algorithm for Resource Management in Service-oriented Systems
... based clustering, overlay construction gets involved in the methods of organizing nodes/resources in the groups without considering the content (for example in terms of attributes, features, properties and ...
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Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self Organizing Map
... The GEO3DSOM on the other hand outperforms the stan- dard SOM in providing a grouping of the data in a spatially coherent way. Analysis of both the artificial and the real life data sets showed that the GEO3DSOM is ...
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