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Dynamic cluster formation using level set methods
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Principal Component Analysis
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Wiley Series in Probability and Statistics
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A Variational Level Set Approach to Multiphase Motion
inferredcites →https://doi.org/10.1007/11564126_12openalex-snapshot
Cluster Aggregate Inequality and Multi-level Hierarchical Clustering
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A Survey of Clustering Data Mining Techniques
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Parallelisation of Sparse Grids for Large Scale Data Analysis
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Principal Component Analysis
inferredcites →https://doi.org/10.1007/b98879openalex-snapshot
Level Set Methods and Dynamic Implicit Surfaces
inferredcites →https://doi.org/10.1007/bf01385685openalex-snapshot
A geometric model for active contours in image processing
inferredcites →https://doi.org/10.1007/bf02614317openalex-snapshot
Cluster analysis and mathematical programming
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WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
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Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
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Fundamentals of digital image processing
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Introduction to Statistical Pattern Recognition
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Geometric Partial Differential Equations and Image Analysis
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Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
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Cross-Validation of Multivariate Densities
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Comprehensive Identification of Cell Cycle–regulated Genes of the YeastSaccharomyces cerevisiaeby Microarray Hybridization
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HYPERGEOMETRIC PARTIAL DIFFERENTIAL EQUATIONS (I)
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Chameleon: hierarchical clustering using dynamic modeling
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An approach to active spatial data mining based on statistical information
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A min-max cut algorithm for graph partitioning and data clustering
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Least squares quantization in PCM
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CLARANS: a method for clustering objects for spatial data mining
inferredcites →https://doi.org/10.1137/1.9781611972733.5openalex-snapshot
Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
inferredcites →https://doi.org/10.1137/1.9781611972740.22openalex-snapshot
Clustering with Bregman Divergences
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Fast Sweeping Algorithms for a Class of Hamilton--Jacobi Equations
inferredcites →https://doi.org/10.1145/276304.276314openalex-snapshot
Automatic subspace clustering of high dimensional data for data mining applications
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Data mining with sparse grids using simplicial basis functions
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On Estimation of a Probability Density Function and Mode
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Finding Groups in Data: An Introduction to Cluster Analysis.
inferredcites →https://doi.org/10.3233/ida-2002-6602openalex-snapshot
Classification with sparse grids using simplicial basis functions
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Finding groups in data: an introduction to cluster analysis
inferredcites →https://doi.org/10.5860/choice.49-3305openalex-snapshot
Data mining: concepts and techniques
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Level Learning Set: A Novel Classifier Based on Active Contour Models
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The Bump Hunting Method Using the Genetic Algorithm with the Extreme-Value Statistics
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New Method for Power System Dynamic Stability Analysis Based on a Novel Unsupervised Clustering Algorithm
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Constructing minimum volume surfaces using level set methods for novelty detection
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Synthesizing Multiple Scan Trees to Optimize Test Application Time
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Supervised learning of classifiers via level set segmentation
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Subclass discriminant analysis using dynamic cluster formation for EEG-based brain-computer interface
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Novelty Detection Using Level Set Methods with Adaptive Boundaries
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Cloudiness analysis in Ukraine by the 3-stages hierarchical clustering algorithm
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Clustering by Local Gravitation
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Evaluating the Stability of Spatial Keypoints via Cluster Core Correspondence Index
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Novelty Detection Using Level Set Methods
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Defending Malicious Check-In Using Big Data Analysis of Indoor Positioning System: An Access Point Selection Approach
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Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
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Scalable Clustering for Mining Local-Correlated Clusters in High Dimensions and Large Datasets
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IMAGE STRUCTURE ANALYSIS BY 3-STAGES CLUSTERING
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HIC: A Robust and Efficient Hyper-Image-Based Clustering for Very Large Datasets
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