Search Results for "clustering algorithm"
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Showing 51 open source projects for "clustering algorithm"
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Clustering.jl
A Julia package for data clustering
Methods for data clustering and evaluation of clustering quality. -
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HDBSCAN
A high performance implementation of HDBSCAN clustering
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select. ... -
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sktime
A unified framework for machine learning with time series
...It features dedicated time series algorithms and tools for composite model building such as pipelining, ensembling, tuning, and reduction, empowering users to apply an algorithm designed for one task to another. -
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Alink
Alink is the Machine Learning algorithm platform based on Flink
Alink is Alibaba’s scalable machine learning algorithm platform built on Apache Flink, designed for batch and stream data processing. It provides a wide variety of ready-to-use ML algorithms for tasks like classification, regression, clustering, recommendation, and more. Written in Java and Scala, Alink is suitable for enterprise-grade big data applications where performance and scalability are crucial. -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is pedagogical: you’ll see linear regression, logistic regression, k-means clustering, neural nets, decision trees, etc., built in Python using fundamentals like NumPy and Matplotlib, not hidden behind API calls. ... -
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ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
ML-For-Beginners is a structured, project-driven curriculum that teaches foundational machine learning concepts with approachable math and lots of code. Organized as a multi-week course, it mixes short lectures with labs in notebooks so learners practice regression, classification, clustering, and recommendation techniques on real datasets. Each lesson aims to connect the algorithm to a relatable scenario, reinforcing intuition before diving into parameters, metrics, and trade-offs. The repository includes quizzes, solutions, and instructor materials to make the content usable in classrooms or self-study. It emphasizes ethical considerations and model evaluation—accuracy is not the only metric—so students learn to validate and communicate results responsibly. ... -
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Smile
Statistical machine intelligence and learning engine
Smile is a fast and comprehensive machine learning engine. With advanced data structures and algorithms, Smile delivers the state-of-art performance. Compared to this third-party benchmark, Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster? Write applications quickly in Java, Scala, or any JVM... -
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AngClust
AngClust: Angle-based feature clustering for time series
.... * We defined three indicators to identify significant clusters: (i) the fluctuation degree of expression levels, (ii) homogeneity, and (iii) the degree of clustering while the clusters are functionally significant. * The clustering outcome of our algorithm (AngClust) is better than the currently most popular STEM algorithm. * AngClust can be used to analyze any short time series gene expression profiles. -
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MScDB
A Mass Spectrometry Centric Protein Sequence Database for Proteomics
...The core modules of MScDB are an in-silico proteolytic digest and a peptide centric clustering algorithm that groups protein sequences that are indistinguishable by mass spectrometry. -
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DeepCluster
Deep Clustering for Unsupervised Learning of Visual Features
DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups. -
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
This repository contains MATLAB / Octave implementations of popular machine learning algorithms, along with explanatory code and mathematical derivations, intended as educational material rather than production code. Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. Code written so as to expose and comment on mathematical steps. The repository includes clustering, regression, classification, neural networks, anomaly detection, and other standard ML topics. Does not rely heavily on specialized toolboxes or library shortcuts. -
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weka-MTreeClusterer
Flat clustering algorithm based on MTrees implemented for weka.
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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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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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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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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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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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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. ... -
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CCKDM
Concern Mining
This is a tool for concern mining which uses a KDM model as input and the output is the same model with annotated concerns. It uses a Concern Library and a modified String Clustering K-means algorithm with Levenshtein metric to cluster the strings. -
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Unsupervised Random Forest
On-line Unsupervised Random Forest
...It supports on-line prediction of new observations (no need to retrain); and supports datasets that contain both continuous (e.g. CPU load) and categorical (e.g. VM instance type) features. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. For the sake of efficiency and meeting the dynamism requirement of autonomic clouds, our methodology consists of two steps: (i) off-line clustering and (ii) on-line prediction. RF+PAM can: Cluster observations (Unsupervised Learning) Calculate the dissimilarity between 2 or more observations (how different two observations are) -
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Fuzzy clustering variation looks for a good subset of attributes in order to improve the classification accuracy of supervised learning techniques in classification problems with a huge number of attributes involved. It first creates a ranking of attributes based on the Variation value, then divide into two groups, last using Verification method to select the best group.Simon Fong, Justin Liang, YanZhuang, "Improving Classification Accuracy Using Fuzzy Clustering Coefficients of Variations (FCCV) Feature Selection Algorithm", 2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI), pp.147-151
