Search Results for "clustering algorithm matlab" - Page 3
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Showing 151 open source projects for "clustering algorithm matlab"
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DSeg software
A MATLAB program to segment filamentous bacteria and hyphae structures
...Here we present an image analysis tool DSeg to overcome the difficulties in finding complicated elongated cell shapes by using time-lapse data and cell morphological constraints. A fast binary level-set based algorithm is implemented for extracting object contour and refining shapes. The software is implemented in MATLAB for segmenting and tracking of cell contours from various microscopy systems. Video instructions: https://www.youtube.com/embed/qMbM0shkk7A?rel Lastest updates: 20190930 DSeg: A dynamic segmentation program to extract backbone patterns for filamentous bacteria and hyphae structures,(2018). ... -
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XBioSiP
RTL & Behavioral Models (Approx.) of Pan-Tompkins Application Stages
The "XBioSiP" library contains the RTL (VHDL) and behavioral (MATLAB) models of the approximate adders and multipliers used for designing approximate versions of the bio-signal processing Pan-Tompkins algorithm, including all of its application stages. This work was published in DAC 2019. In case of usage please refer to: B. S. Prabakaran, S. Rehman, M. Shafique, “XBioSiP: A Methodology for Approximate Bio-Signal Processing at the Edge”, IEEE/ACM 56th Design Automation Conference (DAC), June 2019. -
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spark-ml-source-analysis
Spark ml algorithm principle analysis and specific source code
...The project aims to help developers and data scientists understand how distributed machine learning algorithms are implemented and optimized inside the Spark ecosystem. Instead of providing a runnable software system, the repository focuses on explaining algorithm principles and examining the underlying source code used in Spark’s machine learning package. The repository contains detailed analyses of various algorithms including classification, regression, clustering, dimensionality reduction, and recommendation systems. Each section discusses both the mathematical principles behind the algorithms and how Spark implements them in a distributed computing environment. ... -
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Clustering by Shared Subspaces
Grouping Points by Shared Subspaces for Effective Subspace Clustering
These functions implement a subspace clustering algorithm, proposed by Ye Zhu, Kai Ming Ting, and Mark J. Carman: "Grouping Points by Shared Subspaces for Effective Subspace Clustering", Published in Pattern Recognition Journal at https://doi.org/10.1016/j.patcog.2018.05.027 -
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De_Lux
Deconvolution of luminescence cross-talk in microplate reader data
An algorithm to deconvolve the luminescence cross-talk in high-throughput gene expression profiling to recover the true luminescence activity of a microplate. -
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Distance Scaling
A Distance Scaling Method to Improve Density-Based Clustering
These functions implement a distance scaling method, proposed by Ye Zhu, Kai Ming Ting, and Maia Angelova, "A Distance Scaling Method to Improve Density-Based Clustering", in PAKDD2018 proceedings: https://doi.org/10.1007/978-3-319-93040-4_31. -
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NNC
Nuclear Norm Clustering
We present Nuclear Norm Clustering (NNC), an algorithm that can be used in different fields as a promising alternative to the k-means clustering method, and that is less sensitive to outliers. The NNC algorithm requires users to provide a data matrix M and a desired number of cluster K. We employed simulate annealing techniques to choose an optimal L that minimizes NN(L). -
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MatlabFunc
Matlab codes for feature learning
MatlabFunc is a collection of MATLAB functions developed by the ZJULearning group to support various tasks in computer vision, machine learning, and numerical computation. The repository brings together a wide range of utility scripts, algorithms, and implementations that serve as building blocks for research and development. These functions cover areas such as matrix operations, optimization, data processing, and visualization, making them broadly applicable across different research... -
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Invariant curve calculations in Matlab
Calculating stable & unstable curves for 2 dimensional maps in matlab.
This is an implementation that follows closely the algorithm for calculating stable curves, described by J. P. England, B. Krauskopf, H. M. Osinga in the paper "Computing One-Dimensional Stable Manifolds and Stable Sets of Planar Maps without the Inverse" published in SIAM J. APPLIED DYNAMICAL SYSTEMS 3.2 (2004), 161-190. The package also contains an implementation for calculating the unstable curves which is based on the paper "Growing 1D and Quasi-2D Unstable Manifolds of Maps" by Bernd... -
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spark-msna
Algorithm on Spark for aligning multiple similar DNA/RNA sequences
The algorithm uses suffix tree for identifying common substrings and uses a modified Needleman-Wunsch algorithm for pairwise alignments. In order to improve the efficiency of pairwise alignments, an unsupervised learning based on clustering technique is used to create a knowledge base to guide them. -
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Toolbox
Piotr's Image & Video Matlab Toolbox
Piotr’s Image & Video MATLAB Toolbox is a general-purpose MATLAB toolbox for image and video processing and vision tasks, offering utilities, filters, detection, feature extraction, and algorithm building blocks. Example and demo scripts for usage (e.g. acfReadme, detector readmes). It augments MATLAB’s native capabilities (not replacing the Image Processing Toolbox) by providing efficient, reusable wrappers and optimized routines. -
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popt4jlib
Parallel Optimization Library for Java
popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known... -
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DGRLVQ
Dynamic Generalized Relevance Learning Vector Quantization
Some of the usual problems for Learning vector quantization (LVQ) based methods are that one cannot optimally guess about the number of prototypes required for initialization for multimodal data structures i.e.these algorithms are very sensitive to initialization of prototypes and one has to pre define the optimal number of prototypes before running the algorithm. If a prototype, for some reasons, is ‘outside’ the cluster which it should represent and if there are points of a different... -
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A Generic Platform for Iris Recognition
A framework that allows iris recognition algorithms to be evaluated
This MATLAB based framework allows iris recognition algorithms from all four stages of the recognition process (segmentation, normalisation, encoding and matching) to be automatically evaluated and interchanged with other algorithms performing the same function. The algorithm for each stage can be selected from a list of available algorithms, with selection available for subfunctions as well. -
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The purpose of this program is to provide the user with a convenient algorithm for automatic Independent Component (IC) selection with respect to the contributions of the ICs to a certain event-related brain potential (ERP). www.jan-wessel.de
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Deep Photo Style Transfer
Code and data for paper "Deep Photo Style Transfer"
...The repository provides code in Torch (Lua), MATLAB / Octave scripts for computing the Laplacian, and pre-trained models. Pretrained models and example scripts for ease of use. Compatibility with MATLAB / Octave for Laplacian computations. -
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ECO
Matlab implementation of the ECO tracker
ECO (Efficient Convolution Operators for Tracking) is a high-performance object tracking algorithm developed by Martin Danelljan and collaborators. It is based on discriminative correlation filters and designed to handle appearance changes, occlusions, and scale variations in visual object tracking tasks. The code provides a MATLAB implementation of the ECO and ECO-HC (high-speed) variants and was one of the top performers on multiple visual tracking benchmarks. -
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This package includes a collection of MATLAB files which are designed to: 1. Given a calibration scan of the image of a point emitter with an engineered point spread function (PSF), 2. Perform a phase retrieval algorithm based on maximum likelihood estimation (MLE) of a phase aberration term which is added to the theoretical pupil function of the imaging system. 3. Use the phase-retrieved pupil function to perform single-emitter localization.
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QSdpR
Viral Quasispecies Reconstruction software based on QSdpR algorithm
This is a viral quasispecies reconstruction software for quasispecies assembly problem on mRNA viruses, which is based on a correlation clustering approach and uses semidefinite optimization framework. The software accepts a reference genome, a NGS read set aligned to this reference and set of SNP locations in the form of a vcf file and outputs an optimal set of reconstructed species genomes which describes the underlying viral population. -
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BISD
Batch incremental SNN-DBSCAN clustering algorithm
Incremental data mining algorithms process frequent up- dates to dynamic datasets efficiently by avoiding redundant computa- tion. Existing incremental extension to shared nearest neighbor density based clustering (SNND) algorithm cannot handle deletions to dataset and handles insertions only one point at a time. We present an incremen- tal algorithm to overcome both these bottlenecks by efficiently identify- ing affected parts of clusters while processing updates to dataset in batch mode. -
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node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. ... -
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All future developments will be implemented in the new MATLAB toolbox SciXMiner, please visit https://sourceforge.net/projects/scixminer/ to download the newest version. The former Matlab toolbox Gait-CAD was designed for the visualization and analysis of time series and features with a special focus to data mining problems including classification, regression, and clustering.
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TESTIMAGES
Testing images for scientific purposes
The TESTIMAGES archive is a huge and free collection of sample images designed for analysis and quality assessment of different kinds of displays and image processing techniques. The archive includes more than 2 million images originally acquired and divided in three different categories: SAMPLING and SAMPLING_PATTERNS (aimed at testing resampling algorithms), COLOR (aimed at testing color rendering on different displays) and PATTERNS (aimed at testing the rendering of standard geometrical... -
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Density-ratio based clustering
Discovering clusters with varying densities
This site provides the source code of two approaches for density-ratio based clustering, used for discovering clusters with varying densities. One approach is to modify a density-based clustering algorithm to do density-ratio based clustering by using its density estimator to compute density-ratio. The other approach involves rescaling the given dataset only. An existing density-based clustering algorithm, which is applied to the rescaled dataset, can find all clusters with varying densities that would otherwise impossible had the same algorithm been applied to the unscaled dataset. ...
