A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Neural network architectures: an introduction
Neural network architectures: an introduction
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Subspace clustering for high dimensional categorical data
ACM SIGKDD Explorations Newsletter
Journal of Computational and Applied Mathematics - Special issue: International conference on mathematics and its application
An enhanced ART2 neural network for clustering analysis
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
Indexing density models for incremental learning and anytime classification on data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Clustering: A neural network approach
Neural Networks
Journal of Computational and Applied Mathematics
A fuzzy subspace algorithm for clustering high dimensional data
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Fuzzy partition based soft subspace clustering and its applications in high dimensional data
Information Sciences: an International Journal
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A new neural network architecture (PART) and the resulting algorithm are proposed to find projected clusters for data sets in high dimensional spaces. The architecture is based on the well known ART developed by Carpenter and Grossberg, and a major modification (selective output signaling) is provided in order to deal with the inherent sparsity in the full space of the data points from many data-mining applications. This selective output signaling mechanism allows the signal generated in a node in the input layer to be transmitted to a node in the clustering layer only when the signal is similar to the top-down weight between the two nodes and, hence, PART focuses on dimensions where information can be found. Illustrative examples are provided, simulations on high dimensional synthetic data and comparisons with Fuzzy ART module and PROCLUS are also reported.