cluster centers
A Clustering Algorithm Based on the Combination of MST and Cluster Centers
... algorithms cluster by removing the inconsistent ...novel cluster algorithm based on the combination of MST and cluster centers ...the cluster centers are determined by the ...
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Fitness training driven by image target detection technology
... of cluster centers belonging to the same type of target before and after clustering should not be destroyed, and the relative change is ...the cluster spaces has a large variation range before and ...
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Application of Fuzzy Modeling and Optimization in Enzymatic Esterification Process - Volume 2 Number 6 (Dec. 2011) - IJCEA
... The iteration would be stopped when no further improvement is observed in J(U,V). In general, it is expected that by increasing the number of cluster centers, the accuracy of model would increase. However, ...
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IJCSMC, Vol. 2, Issue. 12, December 2013, pg.288 – 297 RESEARCH ARTICLE An Intelligent System for Mineral Identification using Unsupervised Learning Approach
... Abstract— KSOM is being used to identify classes of mineral in a hyperspectral data. To achieve this, Characterization map was obtained which was clustered using c-means clustering to obtain the characteristics ...
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Vol 7, No 8 (2017)
... and cluster centers helps to assign each point to the nearest cluster center based upon minimum distance then re-compute the new cluster ...any cluster centers. Therefore, new ...
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A new projection method for biological semantic map generation
... Low-dimensional representation is a convenient method of obtaining a synthetic view of complex datasets and has been used in various domains for a long time. When the representation is related to words in a document, ...
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Data analysis by combining the modified k-means and imperialist competitive algorithm
... a cluster from their related cluster centers ...the cluster centers of so that the sum of squared distances of every from the nearest cluster center is ...of cluster ...
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A Framework for Medical Text Mining using a Novel Categorical Clustering Algorithm
... The fast growth of medical records provides new opening for meaningful information retrieval in clinical diagnosis and treatment. Although nursing and pathology records provide a complete account of patient’s information ...
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ENHANCEMENT OF AES ALGORITHM BASED ON CHAOTIC MAPS AND SHIFT OPERATION FOR IMAGE ENCRYPTION
... The experimental results show the accuracy of EFKCN [4] not yet close to 92.7 percent by fourth iteration (Table 1). Moreover, with t = 4, the accuracy of EFKCN is 75.22 percent. Meanwhile, the accuracy of FKCN [1] with ...
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Large Scale Image Classification using High Performance Clustering
... Finally, we tested the full execution of our image classification application. As described in Section II, the first step of constructing our classifiers was to create the vocabulary. To do this, we took a set of 12 ...
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Design Of An Intrusion Detection System Based On Distance Feature Using Ensemble Classifier R.Radhika, B. Sundarraj Abstract PDF IJIRMET1602040017
... combining cluster centers and nearest neighbors (CANN) for novel feature representation for efficient and effective intrusion ...k-means cluster algorithm to extract the cluster centers ...
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Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro Fuzzy Algorithms
... the cluster centers as mem- bership functions in a fuzzy system [3], Also, neuro-fuzzy inference system (ANFIS) with multiple inputs and one output (“Multiple-Inputs-Single-Output” or MISO) is used to ...
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High Performance Clustering of Social Images in a MapCollective Programming Model
... Finally we present a full execution of the image clustering application here. We successfully cluster 7.42 million vectors into 1 million cluster centers. We create 10000 map tasks on 125 nodes. Each ...
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Convergence and rates for fixed interval multiple track smoothing using k means type optimization
... Remark 3.5. Note that in the above theorem we did not need to assume a correct choice of k. If the true number of cluster centers is k and we incorrectly use k = k , then the resulting cluster ...
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Deterministic Annealing
... You restate problem to consider from the start an arbitrary number of cluster centers at each center with pk the density of clusters at site k All these clusters at a given site have sam[r] ...
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Color Image Segmentation using Rough Set based K Means Algorithm
... the cluster centers are determined and then in the next phase they are reduced using ...of cluster centers with the purpose of segmentation of the color (R,G,B components) ...of cluster ...
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A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation
... This paper proposes a three-stage density-based clustering approach to segment urban scene MLS data. First, after filtering out noisy points, the original point cloud dataset is voxelized, and the ground voxels are ...
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An Efficient Approach to Enhance Performance of FCM Using PFA
... update cluster center is given to each type. Thus all the close-cluster groups can find the corresponding solution to update both cluster number and cluster centers with the updated ...
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A Image Segmentation Algorithm Based on Differential Evolution Particle Swarm Optimization Fuzzy C-Means Clustering
... However, this algorithm is influenced by the initial cluster centers and can fall into local optimal value easily. In order to change the disadvantages of the FCM, the major improvement strategies have: the ...
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An Improved Kernel K Mean Cluster Method and Its Application in Fault Diagnosis of Roller Bearing
... K-mean cluster method is run in an implicit feature space, the initial and iterative cluster centers cannot be defined ...initial cluster centers selected in the original space ...
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