A Network Algorithm to Discover Sequential Patterns
... The Sequential Patterns Discovery, which extends itemset mining, is a complex task for the user. The user must specify a minimum support threshold to find the desired patterns. A useless output can be expected by pruning either too many or too few items. The process must be repeated interactively, w ...
... The Sequential Patterns Discovery, which extends itemset mining, is a complex task for the user. The user must specify a minimum support threshold to find the desired patterns. A useless output can be expected by pruning either too many or too few items. The process must be repeated interactively, w ...
Cluster Analysis on High-Dimensional Data: A Comparison of
... Clustering is a powerful exploratory technique for extracting knowledge of given data (Witten, Frank, & Hall, 2011). Clustering is an unsupervised process, because in the clustering process, there are no predefined classes and no examples that would show what kind of desirable relations should be va ...
... Clustering is a powerful exploratory technique for extracting knowledge of given data (Witten, Frank, & Hall, 2011). Clustering is an unsupervised process, because in the clustering process, there are no predefined classes and no examples that would show what kind of desirable relations should be va ...
Using Topic Keyword Clusters for Automatic Document
... consider the aggregate interconnectivity between the two clusters. Restated these algorithms do not consider special properties of individual clusters and, thus may make wrong merging decisions when the underlying data do not follow the assumed model, or when noise is present. Centroid-based cluster ...
... consider the aggregate interconnectivity between the two clusters. Restated these algorithms do not consider special properties of individual clusters and, thus may make wrong merging decisions when the underlying data do not follow the assumed model, or when noise is present. Centroid-based cluster ...
Two-level Clustering Approach to Training Data Instance Selection
... data gathered. The memory or calculation capacities are not sufficient enough because the sizes of data sets are also growing. To tackle this problem, suitable methods have to be developed for selecting suitable instances from the whole data set as training data. In this study clustering was formed ...
... data gathered. The memory or calculation capacities are not sufficient enough because the sizes of data sets are also growing. To tackle this problem, suitable methods have to be developed for selecting suitable instances from the whole data set as training data. In this study clustering was formed ...
WaveCluster: a wavelet-based clustering approach for spatial data
... dataset yields a good clustering, and one or more additional passes can (optionally) be used to improve the quality further. So, the computational complexity of BIRCH is O(N ). BIRCH is also the first clustering algorithm to handle noise [ZRL96]. Since each node in a CF-tree can only hold a limited ...
... dataset yields a good clustering, and one or more additional passes can (optionally) be used to improve the quality further. So, the computational complexity of BIRCH is O(N ). BIRCH is also the first clustering algorithm to handle noise [ZRL96]. Since each node in a CF-tree can only hold a limited ...
Topological visual analysis of clusterings in high
... The ideas to create the high-dimensional point cloud’s density function efficiently and how to analyze it topologically are explained in [20]. We only focus on the output of the topological analysis; the join tree. As indicated in Figure 2, the tree partitions the density function into regions. The ...
... The ideas to create the high-dimensional point cloud’s density function efficiently and how to analyze it topologically are explained in [20]. We only focus on the output of the topological analysis; the join tree. As indicated in Figure 2, the tree partitions the density function into regions. The ...
as a PDF
... candidate subsequence generation. A typical Apriori like method such as GSP [9] adopts a multiple-pass, candidate generation and test approach in sequential pattern mining.In clustering, the algorithms perform an initial division of the data in the clusters and then move the objects from one cluster ...
... candidate subsequence generation. A typical Apriori like method such as GSP [9] adopts a multiple-pass, candidate generation and test approach in sequential pattern mining.In clustering, the algorithms perform an initial division of the data in the clusters and then move the objects from one cluster ...
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.