Clustering High--Dimensional Data
First International Workshop, CHDD 2012, Naples, Italy, May 15, 2012, Revised Selected Papers
Published on 24. November 2015
IX, 149 pages
978-3-662-48577-4 (ISBN)
Description
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.
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Francesco Masulli | Alfredo Petrosino | Stefano Rovetta
Clustering High--Dimensional Data
First International Workshop, CHDD 2012, Naples, Italy, May 15, 2012, Revised Selected Papers
Book
11/2015
Springer
€42.80
Shipment within 7-9 days
Content
- Intro
- Preface
- Organization
- Contents
- Clustering High-Dimensional Data
- 1 Introduction
- 2 Defining Clustering
- 3 The Century of Big Data
- 4 Approaches to High Dimensional Data Clustering
- 4.1 Subspace Clustering
- 4.2 Projected Clustering
- 4.3 Biclustering
- 4.4 Hierarchical Clustering
- 5 Conclusions
- References
- What are Clusters in High Dimensions and are they Difficult to Find?
- 1 Introduction
- 2 Properties of High-Dimensional Data
- 3 Cluster Analysis
- 4 What are Clusters, Especially in Higher Dimensions?
- 5 Consequences for Clustering Algorithms
- 6 Conclusions
- References
- Efficient Density-Based Subspace Clustering in High Dimensions
- 1 Introduction
- 2 Density-Based Subspace Clustering
- 3 Dimensionality Unbiased Density
- 4 Redundancy-Removal
- 5 Pruning Subspace Clusters
- 6 Indexing Subspace Clustering
- 7 Approximate Jump Clustering
- 8 Conclusion
- References
- Comparing Fuzzy Clusterings in High Dimensionality
- 1 Introduction
- 2 Fuzzy Clustering
- 2.1 Some Notations and Definitions
- 2.2 Fuzzy Clustering
- 2.3 Methods for Fuzzy Clustering
- 2.4 Possibilistic Clustering Models
- 2.5 Graded Possibilistic Models
- 3 Comparing Fuzzy Clusterings
- 3.1 Approaches to the Comparison of Clusterings
- 3.2 Notation
- 3.3 Co-association
- 3.4 Fuzzy Coassociation
- 3.5 Comparing Two Partitions
- 4 Partition Similarity Indexes
- 4.1 The Rand and Jaccard Indexes
- 4.2 The Fuzzy Jaccard Index
- 4.3 The Fuzzy Rand Index
- 4.4 The Probabilistic Rand Index
- 4.5 The Probabilistic Jaccard Index
- 5 Applications of Fuzzy Similarity Indexes
- 5.1 Visual Stability Analysis Based on Comparing Fuzzy Clusterings
- 5.2 Tracking Deterministic Annealing
- 6 Conclusion
- References
- Time Series Clustering from High Dimensional Data
- 1 Introduction
- 2 Financial High Dimensional Data Characteristics
- 3 Beanplot Time Series
- 4 Parameterizing Beanplot Time Series Data
- 5 Time Series Factor Analysis on Beanplot Time Series
- 6 From Time Series Factor Analysis to the Feature Clustering Approach
- 7 Using the Self Organizing Maps
- 8 Simulation Study
- 9 Application on Real Data
- 10 Conclusions
- References
- Data Dimensionality Estimation: Achievements and Challanges
- 1 Introduction
- 2 Global Methods
- 2.1 Projection Techniques
- 2.2 Fractal-Based Methods
- 2.3 Multidimensional Scaling and Other Methods
- 3 Local Methods
- 3.1 Fukunaga-Olsen's Algorithm
- 3.2 TRN-Based and Local MDS Methods
- 4 Mixed Methods
- 4.1 Levina-Bickel Algorithm
- 5 ID Estimation Methods Benchmarking
- 6 Conclusions
- References
- A Novel Intrinsic Dimensionality Estimator Based on Rank-Order Statistics
- 1 Introduction
- 2 Related Works
- 3 Theoretical Results
- 4 The Algorithm
- 5 Algorithm Evaluation
- 5.1 Dataset Description
- 5.2 Experimental Setting
- 5.3 Experimental Results
- 6 Conclusions and Future Works
- A Algorithm Implementation
- References
- Dimensionality Reduction in Boolean Data: Comparison of Four BMF Methods
- 1 Matrix Decompositions, Dimensionality Reduction, and Boolean Data
- 2 Boolean Matrix Factorization
- 3 The Four Methods Being Compared
- 4 Experimental Comparison
- 4.1 Method of Comparison
- 4.2 Datasets Used
- 4.3 Results
- 5 Conclusions and Further Issues
- References
- A Rough Fuzzy Perspective to Dimensionality Reduction
- 1 Introduction
- 2 Related Works
- 3 Rough-Fuzzy Sets
- 4 Rough-Fuzzy Product Feature Selection
- 4.1 Feature Granularization
- 4.2 Feature Selection
- 5 Experimental Results
- 6 Conclusions
- References
- Author Index
