Improving seabed mapping from marine acoustic data
... Often inappropriate to use the traditional analysis methods (statistics, data mining, etc.) for spatial data. The two basic assumptions may not be valid: spatial data are not independently generated, nor are they identically distributed. Special analysis methods: spatial analysis, geocomputation, ge ...
... Often inappropriate to use the traditional analysis methods (statistics, data mining, etc.) for spatial data. The two basic assumptions may not be valid: spatial data are not independently generated, nor are they identically distributed. Special analysis methods: spatial analysis, geocomputation, ge ...
A Comparative Performance Analysis of Clustering Algorithms
... number of groups. Several clustering techniques are there: partitioning methods, hierarchical methods, density based methods, grid based methods, model based methods, methods for high dimensional data and constraint based clustering. Clustering is also called data segmentation because clustering par ...
... number of groups. Several clustering techniques are there: partitioning methods, hierarchical methods, density based methods, grid based methods, model based methods, methods for high dimensional data and constraint based clustering. Clustering is also called data segmentation because clustering par ...
featureselection.asu.edu
... – LD performed statistically worse than Lin on datasets Splice and Tic-tac-toe but better than Lin on datasets Connection-4, Hayes and Balance Scale. – LD performed statistically worse than VDM only on one dataset (Splice) but better on two datasets (Connection-4 and Tic-tac-toe). – Finally, LD perf ...
... – LD performed statistically worse than Lin on datasets Splice and Tic-tac-toe but better than Lin on datasets Connection-4, Hayes and Balance Scale. – LD performed statistically worse than VDM only on one dataset (Splice) but better on two datasets (Connection-4 and Tic-tac-toe). – Finally, LD perf ...
Finding and Visualizing Subspace Clusters of High Dimensional
... International Conference on Intelligent Computational Systems (ICICS'2012) Jan. 7-8, 2012 Dubai ...
... International Conference on Intelligent Computational Systems (ICICS'2012) Jan. 7-8, 2012 Dubai ...
Class_Cluster
... fitting N-1 lines. In this case we first learned the line to (perfectly) discriminate between Setosa and Virginica/Versicolor, then we learned to approximately discriminate between Virginica and ...
... fitting N-1 lines. In this case we first learned the line to (perfectly) discriminate between Setosa and Virginica/Versicolor, then we learned to approximately discriminate between Virginica and ...
Behavior of proximity measures in high dimensions
... in high dimensions, and typically, clustering algorithms use distance or similarity measures that work better for high dimensional data e.g., the cosine measure. However, even the use of similarity measures such as the cosine measure does not eliminate all problems with similarity. Specifically, po ...
... in high dimensions, and typically, clustering algorithms use distance or similarity measures that work better for high dimensional data e.g., the cosine measure. However, even the use of similarity measures such as the cosine measure does not eliminate all problems with similarity. Specifically, po ...
PDF
... relationships and patterns within large datasets that previously have no organization. This technique has been used in complicated task such as, pattern recognition, image analysis, and facility location. They are able to use clustering in order to partition the data into homogenous clusters, which ...
... relationships and patterns within large datasets that previously have no organization. This technique has been used in complicated task such as, pattern recognition, image analysis, and facility location. They are able to use clustering in order to partition the data into homogenous clusters, which ...
1: Recent advances in clustering algorithms: a review
... based on concepts and apply an Apriori paradigm for discovering frequent concepts then frequent concepts are used to create clusters. It was found that FCDC algorithm is more efficient and accurate than other clustering algorithms in this application I. ...
... based on concepts and apply an Apriori paradigm for discovering frequent concepts then frequent concepts are used to create clusters. It was found that FCDC algorithm is more efficient and accurate than other clustering algorithms in this application I. ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.