dimensional data

If the objective is to perform multidimensional data analysis, a dimensional data model would be the only choice here.

Experiment results prove that this refined algorithm has better clustering results on multi-dimensional data set than SNN algorithm.

Now, with a plethora of high-dimensional data available, such work can be applied to real-world scenarios.

An R-Tree data structure stores information about multi-dimensional data and is used for indexes on spatial or geographical data.

Absrtact: The problem of similarity measurement between high dimensional data is one of the problems high-dimensional data mining faces.

The Locally Linear Embedding(LLE) algorithm is an effective technique for nonlinear dimensionality reduction of high-dimensional data.

BACKGROUND: Two-dimensional data colourings are an effective medium by which to represent three-dimensional data in two dimensions.

At present, how to deal with large-scale high-dimensional data set is one of the hot and difficult issues in data mining.

Describing topological relationships between large amounts of metabolites, the metabolic networks are abstract, high-dimensional data.

The emergence of Scientific Visualization technology makes possible of three-dimensional data in three-dimensional interpretation.

Neighborhood selection is one of the most important link in low-dimensional representations of high-dimensional data sets.

The data set is projected into low dimensional PCA space using PCA, and the low dimensional data set used as training data set of RVM.

Pattern recognition in multi-dimensional data classification is an import- ant research topic.

Fourthly, using MDX language and ADO MD as a multi-dimensional data access interface to visit scientific research data.

Maintenance management in the tobacco industry introduction to operation cost, the cost of access to multi-dimensional data.

CART decision tree has outstanding advantages on the issue of classification in dealing with multi-dimensional data with complex structure.

Proposed dynamic clustering algorithm has strong robustness in clustering of time series multi-dimensional data.

The query model defines the concepts of dimension selections, edges, cube views, and the aggregations and manipulations of dimensional data.

A new visualizing technique for multi-dimensional data is proposed, which effectively integrates the information of typhoon.

Then, it uses multi-dimensional data modeling technology to improve a logistics data warehouse information model.

The method is a new way of thinking. The experiment results show good in solving the multi-dimensional data.

Designing a dimensional data warehouse is a complex subject, and I have just touched the surface of it in these three articles.

Designed to deliver to Perl the ability to compactly store and speedily manipulate large N-dimensional data arrays.

In recent years, with the application of clustering, high dimensional data clustering is becoming more common, and more important.

A multidimensional visualization technique that creates a 3D representation of n-dimensional data is proposed.

A dimensional data model might also be a good choice because it is user-friendly and has better performance.

A dynamic clustering algorithm was proposed based on consistent matrix of dependent function for time series multi-dimensional data.

Then, on the basis of above theoretical research, the paper focuses on the three-dimensional data processing system design.

Data mining is about digging up interesting information from this high-dimensional data.

This paper presented a method of construction of multi-dimensional data model based on relation databases and XML technology.

Aiming at the similarity measurement of high dimensional data, the paper put forward a new method based on subspace.

Based on the mean shift clustering algorithm, the transfer function for three dimensional data set is designed.

The consistency of BICC in high dimensional data model selection is also shown.

The complexity of feature selection for real-world data stream will increase because of high-dimensional data and concept drifting.

It can project high dimensional data to lower one.

At the same time, this method do the non-dimensional data of treatment, and influence factors will lead to assimilation.

Then it construct data warehouse and make the multi-dimensional data set of customers category, service type and time and so on.

The system consists of three-dimensional data acquisition program and three-dimensional point cloud display program;

Involute cylindrical gears--Information of the dimensional data to be given on the drawing

many false hits were caused when use dimensionality reduction methods or approximate vector technology in high-dimensional data retrieval;

Bevel gears-information of the dimensional data to be given on the drawing

Influence of refractive index mismatch on data bit in two-photon three-dimensional data storage

Cylindrical worm and wormwheel information of the dimensional data to be given on the drawing

An Efficient Discovering and Maintenance Algorithm of Subspace Clustering over High Dimensional Data Streams

Implementing Three- dimensional Data Management of Mineral Resources in Jiangsu Province and Inspiration from It

A three-dimensional data acquisition system used in air exploration balloon

Application of Interesting Subspace Mining Algorithm in High-dimensional Data Clustering

The application of the kinetic-spectrum two-dimensional data in the quantitative analysis of the gray system

Characteristics and Development Trend of Four-dimensional Data Assimilation

second, three-dimensional data that describe the population-averaged folding of chromatin inside cells and;

Structure and method used in dimensional data searching in PCB design

Improving the Similarity Search Performance in High-Dimensional Data Space by Satellite Region

Efficient algorithm for frequent closed patterns mining from high dimensional data

Study on the Low-Dimensional Embedding and the Embedding Dimensionality of Manifold of High-Dimensional Data

Statistical Information Gain of n-dimensional Data with Applications

The study of typhoon process: discussion and application of four-dimensional data assimilation

Three-dimensional Data Visualization and Application in MATLAB

Research on Clustering and Evolution Analysis of High Dimensional Data Stream

Collection and Reconstruction of Three-Dimensional Data on Rough Diamond

A Grid-based Subspace Clustering Algorithm for High-dimensional Data Streams

Research on Methods of High - dimensional Data Visualization

Key Dimension Based High-Dimensional Data Partition Strategy

Dynamic Selection of Materialized Views of Multi-Dimensional Data

Subspace clustering method for high dimensional data stream

An Efficient Clustering Algorithm of Large Scale and High Dimensional Data Set

This platform is used to undertake to dimensional data editor, management and analytic;

The Dynamics and Temperature Field Three-dimensional Data of Engine Valve

Clustering Pyramid-Tree: A New Index Structure for High-Dimensional Data Spaces

Fuzzy Nearest Neighbor Clustering of High-Dimensional Data

Reparation of Three-dimensional Data in Rapid Prototyping

A Mapping- based Clustering Approach for High Dimensional Data

Three-dimensional Data Sequences in Grey System Theory

LIFT: An Index Structure for High Dimensional Data

A Way to Visualize High Dimensional Data of Large Relational Data Sets

A Test for Rigid Structure of the Grouped Points in High-Dimensional Data

Splitting Strategy for High Dimensional Data Based on Fuzzy Clustering

High-dimensional data feature selection based on relevance analysis and GA

Study on the index of high dimensional data space

CBFrag-Cubing: An Algorithm of High-Dimensional Data Cubing Based on Compressed Bitmap

A High Dimensional Data Indexing Technique Based on Max Gap Space Mapping