high-dimensional data set
Kohonen Self Organizing Map with Modified K-means clustering For High Dimensional Data Set
... for data clustering in the field of data ...for data clustering purposes but none can be as fast and accurate as the K-Means ...clustering high dimensional data set ...
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An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality
... training set are ...classify data without any external supervision ...two dimensional data collection is repeatedly presented to the SOM until a topology preserving mapping from the multi ...
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PERFORMANCE ANALYSIS OF WLAN UNDER VARIABLE NUMBER OF NODES USING THE ADJUSTABLE PARAMETERS IN EDCA
... generating high dimensional data sets by capturing millions of facts in various fields, time phases, localities and ...Microarray data contains gene expression from thousands of genes ...
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A Two Sample Test for High Dimensional Data with Applications to Gene set Testing
... was high at 95% and 99%, the proposed test still were much more powerful than the two multiple testing procedures for all three allocations of the non-zero components in µ 2 ...
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Clustering of High-Dimensional Data Using Hubness
... a set of objects in such a way that objectsin the same group are more similar to each other than to those in other groups ...in data mining and image ...lower dimensional feature ...such data, ...
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MVS Clustering of Sparse and High Dimensional Data
... Since shown with the over evaluation, nearness of two stories di along with dj : simply because they come in a similar number : can be recognized because normal of similitudes scored usually on the viewpoints of all ...
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A Survey on High Dimensional Data Classification in Booster
... of data examination, gathering expect a basic part in finding the essential case structure introduced in unlabeled ...the data into the memory for examination get to be particularly infeasible when the ...
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Clustering Algorithms for High Dimensional Data – A Survey
... Bara’a Ali Attea et al. discovered that performance of clustering algorithms degrades with more and more overlaps among clusters in a data set. These facts have motivated to develop a fuzzy multi-objective ...
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Modelling Interactions in High-dimensional Data with Backtracking
... This data set available at ...FBI data, and national census data from 1990, for various towns and communities around the ...The data set contains two different estimates of the ...
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Cluster based boosting for high dimensional data
... In this paper, we discussed and explained various boosting problem and proposed solutions and also described some clustering techniques. Boosting proved advantageous for more accurate results in machine learning. Cluster ...
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Machine learning techniques for high dimensional data
... a set of data samples, without explicit knowledge of the identifies or classes of the ...original set when determining the mapping functions, but not the local ...original set to preserve the ...
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Improving Efficiency In High Dimensional Data Sets
... in high dimensional information with few perceptions are ending up more typical, particularly in microarray ...preparation set, particularly with high dimensional ...
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A new approach for data visualization problem
... multidimensional data making use of humans’ natural visual ...specifically, data visualization reveals relationships in data sets that are not evident from the raw data, by using mathematical ...
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New approaches for clustering high dimensional data
... a data analysis tool, it is closely coupled with visualization in the follow- ing ...the set of SSCs, since they believe they belong to the same cluster, or they want to force to divide a cluster into ...
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Bayesian kernel projections for classification of high dimensional data
... The data set is made up of images that are a sub- set of the Corel database, which contains 59,795 images of a wide variety of scenes, 8,114 of which are of ani- ...
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Dimension Reduction and Classification for High Dimensional Complex Data.
... DTI, which is a relatively new imaging technique, can be used to visualize and mea- sure the diusion of water in brain tissue. It is especially useful for examining white matter abnormalities in the brain. As such, DTI ...
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Contributions to Statistical Methods for High Dimensional and Dependent Data.
... MAQC-II data described in Section 1.2. The original data have been standard- ized for each ...the data into an equally balanced training set with 50 samples with positive ER status and 50 ...
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K Means Based Clustering In High Dimensional Data
... clustering high dimensional data. [1]High dimensional data is an challenge for clustering algorithms because of the implicit sparsity of the ...clustering data points is ...
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Survey on Clustering High Dimensional data using Hubness
... given data set, then its points are lying approximately on a hypersphere centered at the data ...if data is drawn from several distributions, as is usually the case in clustering problems, ...
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High Performance Multidimensional Scaling for Large High Dimensional Data Visualization
... pubChem data, which is represented by a vector format, we also experimented on the proposed algorithm with other real data sets, which contains 30,000 biological se- quence data with respect to the ...
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