cluster - Data Warehousing and Data Mining by Gopinath N
... do not scale well: time complexity of at least O(n ), where n is the number of total objects can never undo what was done previously Integration of hierarchical with distance-based clustering BIRCH (1996): uses CF-tree and incrementally adjusts the quality of sub-clusters CURE (1998): select ...
... do not scale well: time complexity of at least O(n ), where n is the number of total objects can never undo what was done previously Integration of hierarchical with distance-based clustering BIRCH (1996): uses CF-tree and incrementally adjusts the quality of sub-clusters CURE (1998): select ...
A novel algorithm for fast and scalable subspace clustering of high
... would be that such redundant lower dimensional clusters are not generated at all, as their generation and pruning later on, leads to the higher computational cost. In other words, the subspace clustering algorithm should output only the maximal subspace clusters. As discussed earlier, a cluster is i ...
... would be that such redundant lower dimensional clusters are not generated at all, as their generation and pruning later on, leads to the higher computational cost. In other words, the subspace clustering algorithm should output only the maximal subspace clusters. As discussed earlier, a cluster is i ...
Web People Search via Connection Analysis
... Disambiguation algorithm Correlation Clustering (1/3) • CC has been applied in the past to group documents of the same topic and to other problems. • It assumes that there is a similarity function s(u, v) learned on the past data. • Each (u, v) edge is assigned a “+” (similar) or “-” (different) la ...
... Disambiguation algorithm Correlation Clustering (1/3) • CC has been applied in the past to group documents of the same topic and to other problems. • It assumes that there is a similarity function s(u, v) learned on the past data. • Each (u, v) edge is assigned a “+” (similar) or “-” (different) la ...
Spatio-temporal clustering
... Kriegel, Sander, and Xu) as a heuristic for determination of the input parameters was used in both approaches. Hence, in the first step, the k-dist graph was created using spatial and temporal dimensions. By means of the graph, the analyst could infer the suitable thresholds for the spatial and tempo ...
... Kriegel, Sander, and Xu) as a heuristic for determination of the input parameters was used in both approaches. Hence, in the first step, the k-dist graph was created using spatial and temporal dimensions. By means of the graph, the analyst could infer the suitable thresholds for the spatial and tempo ...
Detecting Outliers in Data streams using Clustering Algorithms
... experimental results confirm that the quality of clusters produced by CURE is much better than those found by existing algorithms. Moreover, the authors expressed the partitioning and random sampling enable CURE to not only outperform existing algorithms but also to scale well for large databases wi ...
... experimental results confirm that the quality of clusters produced by CURE is much better than those found by existing algorithms. Moreover, the authors expressed the partitioning and random sampling enable CURE to not only outperform existing algorithms but also to scale well for large databases wi ...
Optimizing the Accuracy of CART Algorithm
... and knowledge management technique used in grouping similar data objects together. There are many classification algorithms available in literature but decision tree is the most commonly used because of its ease of execution and easier to understand compared to other classification algorithms. The I ...
... and knowledge management technique used in grouping similar data objects together. There are many classification algorithms available in literature but decision tree is the most commonly used because of its ease of execution and easier to understand compared to other classification algorithms. The I ...
A Survey on Time Series Data Mining
... They converted theshape data into a sequential one. The aim is to find sub series, or shapelets as they called them that are discriminating between classes. To determine which subseries are to bechosen, they ordered all series according to their (Euclidean) distance from all possibleshapelets. Then ...
... They converted theshape data into a sequential one. The aim is to find sub series, or shapelets as they called them that are discriminating between classes. To determine which subseries are to bechosen, they ordered all series according to their (Euclidean) distance from all possibleshapelets. Then ...
Temporal Sequence Classification in the Presence
... at different levels of abstraction, i. To obtain the D0 enhanced partition, we simply apply the taxonomy T to each element of the time series instances in the training dataset D. This way we are abstracting the elements of the time series in each particular time instance. Afterwards, our Learner can ...
... at different levels of abstraction, i. To obtain the D0 enhanced partition, we simply apply the taxonomy T to each element of the time series instances in the training dataset D. This way we are abstracting the elements of the time series in each particular time instance. Afterwards, our Learner can ...
The Role of Hubness in Clustering High-Dimensional Data
... notion of density, and illustrate the different relationships they exhibit in low- and high-dimensional settings, we performed additional simulations. For a given number of dimensions (5 or 100), we generated a random Gaussian distribution centered around zero and started drawing random points from ...
... notion of density, and illustrate the different relationships they exhibit in low- and high-dimensional settings, we performed additional simulations. For a given number of dimensions (5 or 100), we generated a random Gaussian distribution centered around zero and started drawing random points from ...
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.