A New Unsupervised Learning for Clustering Using Geometric Associative Memories
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A Novel Path-Based Clustering Algorithm Using Multi-dimensional Scaling
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
New Labeling Strategy for Semi-supervised Document Categorization
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Combining multiple clusterings using similarity graph
Pattern Recognition
MEC --Monitoring Clusters' Transitions
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Self-adjust local connectivity analysis for spectral clustering
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Bipartite graphs for monitoring clusters transitions
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Incremental entity resolution on rules and data
The VLDB Journal — The International Journal on Very Large Data Bases
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The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms in to taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes cluster analysis (unsupervised learning) from discriminant analysis (supervised learning). The objective of cluster analysis is to simply find a convenient and valid organization of the data, not to establish rules for separating future data into categories.