Clustering High--Dimensional Data

Name: Clustering High--Dimensional Data | First International Workshop, CHDD 2012, Naples, Italy, May 15, 2012, Revised Selected Papers
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First International Workshop, CHDD 2012, Naples, Italy, May 15, 2012, Revised Selected Papers

Francesco Masulli Alfredo Petrosino Stefano Rovetta(Herausgeber*in)

Springer (Verlag)

Erschienen am 24. November 2015

IX, 149 Seiten

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Beschreibung

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Inhalt

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

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