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An Interactive Visualization Model for Large Highdimensional Datasets
"... Abstract Data visualization gives a direct view of complex data, which is especially helpful for analysis of large high dimensional datasets. However, existing methods often lose simplicity and clarity while rendering large amount of complex data. In this paper, we discuss some essential properties ..."
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Cited by 1 (1 self)
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Abstract Data visualization gives a direct view of complex data, which is especially helpful for analysis of large high dimensional datasets. However, existing methods often lose simplicity and clarity while rendering large amount of complex data. In this paper, we discuss some essential properties
Iconbased Visualization of Large HighDimensional Datasets
 In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM’03
, 2003
"... High dimensional data visualization is critical to data analysts since it gives a direct view of original data. We present a method to visualize large amount of high dimensional data. We divide dimensions of data into several groups. Then, we use one icon to represent each group, and associate visua ..."
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Cited by 2 (2 self)
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High dimensional data visualization is critical to data analysts since it gives a direct view of original data. We present a method to visualize large amount of high dimensional data. We divide dimensions of data into several groups. Then, we use one icon to represent each group, and associate
Volume Visualization and Visual Queries for Large highdimensional datasets
, 2004
"... We propose a flexible approach for the visualization of large, highdimensional datasets. The raw, highdimensional data is mapped into an abstract 3D distance space using the FastMap algorithm, which helps, together with other linear preprocessing steps, to make changes to the resulting 3D represent ..."
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Cited by 4 (1 self)
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We propose a flexible approach for the visualization of large, highdimensional datasets. The raw, highdimensional data is mapped into an abstract 3D distance space using the FastMap algorithm, which helps, together with other linear preprocessing steps, to make changes to the resulting 3D
SmartSample: An Efficient Algorithm for Clustering Large HighDimensional Datasets
"... Finding useful related patterns in a dataset is an important task in many interesting applications. In particular, one common need in many algorithms, is the ability to separate a given dataset into a small number of clusters. Each cluster represents a subset of datapoints from the dataset, which a ..."
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sample clusters successfully large highdimensional datasets. In addition, smartsample outperforms other methodologies in terms of runningtime. A variation of the smartsample algorithm, which guarantees efficiency in terms of I/O, is also presented. We describe how to achieve an approximation of the in
Exploring Constraints to Efficiently Mine Emerging Patterns from Large Highdimensional Datasets
, 2000
"... Emerging patterns (EPs) were proposed recently to capture changes or differences between datasets: an EP is a multivariate feature whose support increases sharply from a background dataset to a target dataset, and the support ratio is called its growth rate. Interesting long EPs often have low suppo ..."
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Cited by 32 (5 self)
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EPMiner can efficiently mine all EPs at low support on large highdimensional datasets, with low minim...
Automatic Subspace Clustering of High Dimensional Data
 Data Mining and Knowledge Discovery
, 2005
"... Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, enduser comprehensibility of the results, nonpresumption of any canonical data distribution, and insensitivity to the or ..."
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Cited by 724 (12 self)
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identical results irrespective of the order in which input records are presented and does not presume any specific mathematical form for data distribution. Through experiments, we show that CLIQUE efficiently finds accurate clusters in large high dimensional datasets.
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 783 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
The Xtree: An index structure for highdimensional data
 In Proceedings of the Int’l Conference on Very Large Data Bases
, 1996
"... In this paper, we propose a new method for indexing large amounts of point and spatial data in highdimensional space. An analysis shows that index structures such as the R*tree are not adequate for indexing highdimensional data sets. The major problem of Rtreebased index structures is the over ..."
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Cited by 592 (17 self)
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In this paper, we propose a new method for indexing large amounts of point and spatial data in highdimensional space. An analysis shows that index structures such as the R*tree are not adequate for indexing highdimensional data sets. The major problem of Rtreebased index structures
Fastmap: A fast algorithm for indexing, datamining and visualization of traditional and multimedia datasets
, 1995
"... A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in kd space, using k featureextraction functions, provided by a domain expert [Jag91]. Thus, we can subsequently use highly finetuned spatial access methods (SAMs), to answer several ..."
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Cited by 502 (22 self)
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A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in kd space, using k featureextraction functions, provided by a domain expert [Jag91]. Thus, we can subsequently use highly finetuned spatial access methods (SAMs), to answer several
Efficient Clustering of HighDimensional Data Sets with Application to Reference Matching
, 2000
"... Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when the dataset has either (1) a limited number of clusters, (2) a low feature dimensionality, or (3) a sm ..."
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Cited by 338 (15 self)
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technique for clustering these large, highdimensional datasets. The key idea involves using a cheap, approximate distance measure to efficiently divide the data into overlapping subsets we call canopies. Then clustering is performed by measuring exact distances only between points that occur in a common
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