Clustering High--Dimensional Data

This book constitutes the proceedings of the International Workshop on Clustering High-Dimensional Data, CHDD 2012, held in Naples, Italy, in May 2012.

The 9 papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the general subject and issues of high-dimensional data clustering; present examples of techniques used to find and investigate clusters in high dimensionality; and the most common approach to tackle dimensionality problems, namely, dimensionality reduction and its application in clustering. 

• big data clustering
• dimensionality reduction
• high dimensional data analysis
• machine learning
• time series analysis
• anomaly detection
• cluster analysis
• clustering
• data mining
• efficiency
• exploratory data analysis
• inductive inference
• manifold learning
• modeling hierarchies
• rank-order statistics
• rough fuzzy sets
• self-organizing maps
• subspace clustering
• unsupervised feature selection
• unsupervised models of learning

• DIBRIS, University of Genoa DIBRIS, Genoa, Italy

  Francesco Masulli

• University of Naples "Parthenope", Naples, Italy

  Alfredo Petrosino

• DIBRIS, University of Genoa, Genoa, Italy

  Stefano Rovetta

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