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Visualizations for high dimensional data mining  table visualizations
, 1997
"... Visualizations that can handle flat files, or simple table data are most often used in data mining. In this paper we survey most visualizations that can handle more than three dimensions and fit our definition of Table Visualizations. We define Table Visualizations and some additional terms needed f ..."
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Cited by 2 (0 self)
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Visualizations that can handle flat files, or simple table data are most often used in data mining. In this paper we survey most visualizations that can handle more than three dimensions and fit our definition of Table Visualizations. We define Table Visualizations and some additional terms needed
Relationshipbased Clustering and Cluster Ensembles for Highdimensional Data Mining
, 2002
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RelationshipBased Clustering and Visualization for HighDimensional Data Mining
 INFORMS Journal on Computing
, 2002
"... In several reallife datamining... This paper proposes a relationshipbased approach that alleviates both problems, sidestepping the "curseofdimensionality" issue by working in a suitable similarity space instead of the original highdimensional attribute space. This intermediary simil ..."
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Cited by 44 (10 self)
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In several reallife datamining... This paper proposes a relationshipbased approach that alleviates both problems, sidestepping the "curseofdimensionality" issue by working in a suitable similarity space instead of the original highdimensional attribute space. This intermediary
High Dimensional Data Mining in Time Series by Reducing Dimensionality and
"... Time series data is sequence of well defined numerical data points in successive order, usually occurring in uniform intervals. In other words a time series is simply a sequence of numbers collected at regular intervals over a period of time. For example the daily prices of a particular stock can be ..."
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Cited by 1 (0 self)
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mining and knowledge discovery due to their high dimensionality nature. By using symbolic representation of time series data we reduce their dimensionality and numerosity so as to overcome the problems of high dimensional databases. We can achieve the goal of time series data mining by introducing a
Evaluating a Clique Partitioning Problem Model for Clustering HighDimensional Data Mining
, 2004
"... This paper considers the problem of clustering high dimensional data as a clique partitioning problem. Data objects within a cluster have high degree of similarity. The similarity index values are first constructed into a graph as a clique partitioning problem which can be formulated into a form of ..."
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Cited by 1 (0 self)
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This paper considers the problem of clustering high dimensional data as a clique partitioning problem. Data objects within a cluster have high degree of similarity. The similarity index values are first constructed into a graph as a clique partitioning problem which can be formulated into a form
Chapter MINING HIGHDIMENSIONAL DATA
"... Abstract: With the rapid growth of computational biology and ecommerce applications, highdimensional data becomes very common. Thus, mining highdimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, includi ..."
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Abstract: With the rapid growth of computational biology and ecommerce applications, highdimensional data becomes very common. Thus, mining highdimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions
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
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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propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length
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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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
PrivacyPreserving Data Mining
, 2000
"... A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models with ..."
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Cited by 844 (3 self)
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A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models
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