high dimensional data

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

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.

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.

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

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

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

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

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.

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

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

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

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

Research on Clustering and Evolution Analysis of High Dimensional Data Stream

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

Subspace clustering method for high dimensional data stream

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

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

Fuzzy Nearest Neighbor Clustering of High-Dimensional Data

A Mapping- based Clustering Approach for High Dimensional Data

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