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Geometrical and topological approaches to Big Data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Persistent homology analysis of protein structure, flexibility, and folding
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The Elements of Statistical Learning
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A Topological Characterization of Protein Structure
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System Signatures and their Applications in Engineering Reliability
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Open Problems in Spectral Dimensionality Reduction
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Fourth Industrial Revolution
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Pattern Discovery for High-Dimensional Binary Datasets
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Dimension Reduction Methods
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Topological Methods in Data Analysis and Visualization II
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Efficient Computation of Persistent Homology for Cubical Data
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javaPlex: A Research Software Package for Persistent (Co)Homology
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The Gudhi Library: Simplicial Complexes and Persistent Homology
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Topological and Statistical Methods for Complex Data : Tackling Large-Scale, High-Dimensional, and Multivariate Data Spaces
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Reeb Graphs: Approximation and Persistence
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A Mayer–Vietoris Formula for Persistent Homology with an Application to Shape Recognition in the Presence of Occlusions
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Discrete Morse Theoretic Algorithms for Computing Homology of Complexes and Maps
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Industry 4.0 as a Cyber-Physical System study
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Binary Digital Image Processing
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Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
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The rise of “big data” on cloud computing: Review and open research issues
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Object-oriented persistent homology
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Towards Socio-Cyber-Physical Systems in Production Networks
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Topological pattern recognition for point cloud data
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Topological methods for exploring low-density states in biomolecular folding pathways
inferredcites →https://doi.org/10.1088/1742-5468/2009/03/p03034openalex-snapshot
Persistent homology of complex networks
inferredcites →https://doi.org/10.1090/psapm/070openalex-snapshot
Advances in Applied and Computational Topology
inferredcites →https://doi.org/10.1090/s0002-9947-1925-1501318-xopenalex-snapshot
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inferredcites →https://doi.org/10.1090/s0273-0979-07-01191-3openalex-snapshot
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Persistence Theory: From Quiver Representations to Data Analysis
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Design Principles for Industrie 4.0 Scenarios
inferredcites →https://doi.org/10.1109/icsmc.1994.399796openalex-snapshot
Proceedings of IEEE International Conference on Systems, Man and Cybernetics
inferredcites →https://doi.org/10.1109/icsmc.2012.6378022openalex-snapshot
Soft identification of experts in DBLP using FCA and fuzzy rules
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inferredcites →https://doi.org/10.1109/tmag.2013.2294942openalex-snapshot
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inferredcites →https://doi.org/10.1109/tvcg.2011.177openalex-snapshot
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inferredcites →https://doi.org/10.1109/tvcg.2011.239openalex-snapshot
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Multivariate Data Analysis Using Persistence-Based Filtering and Topological Signatures
inferredcites →https://doi.org/10.1111/cgf.12734openalex-snapshot
Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment
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Changing the Equation on Scientific Data Visualization
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Matrix Methods in Data Mining and Pattern Recognition
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Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
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An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities
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An Illustrated Introduction to Topology and Homotopy
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Manifold Learning Theory and Applications
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An Illustrated Introduction to Topology and Homotopy
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Big Graph Storage, Processing and Visualization
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Persistent Topology and Metastable State in Conformational Dynamics
inferredcites →https://doi.org/10.1371/journal.pone.0126383openalex-snapshot
Topological Data Analysis of Biological Aggregation Models
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Blind Swarms for Coverage in 2-D
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Dimension Reduction: A Guided Tour
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Dimension Reduction: A Guided Tour
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A Survey of Dimension Reduction Techniques
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Relations Between the Critical Points of a Real Function of n Independent Variables
inferredcites →https://doi.org/10.2307/41703503openalex-snapshot
Business Intelligence and Analytics: From Big Data to Big Impact
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How Much Information Is There in the World?
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A statistical approach to persistent homology
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A fast algorithm for constructing topological structure in large data
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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