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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 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 45 (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
Relationshipbased Clustering and Cluster Ensembles for Highdimensional Data Mining
, 2002
"... ..."
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
Wang et al. Evaluating a Clique Partitioning Problem Model for Clustering Evaluating a Clique Partitioning Problem Model for Clustering HighDimensional Data Mining
"... 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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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 (15 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 766 (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
Probabilistic Roadmaps for Path Planning in HighDimensional Configuration Spaces
 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
, 1996
"... A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose edg ..."
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Cited by 1276 (124 self)
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A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (=150 MIPS), after learning for relatively short periods of time (a few dozen seconds)
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