Search Results for "em clustering algorithm"
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Showing 45 open source projects for "em clustering algorithm"
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SKUDONET ADC, operates at the application layer, efficiently distributing network load and application load across multiple servers. This not only enhances the performance of your application but also ensures that your web servers can handle more traffic seamlessly.
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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... -
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sktime
A unified framework for machine learning with time series
sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation, and forecasting. It comes with time series algorithms and scikit-learn compatible tools to build, tune and validate time series models. Our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides a unified... -
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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... -
From improving customer experience through seamless sign-on to making MFA as easy as a click of a button – your login box must find the right balance between user convenience, privacy and security.
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MScDB
A Mass Spectrometry Centric Protein Sequence Database for Proteomics
... theoretical and empirical information from large-scale proteomic data to generate a mass spectrometry centric protein sequence database (MScDB). 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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AngClust
AngClust: Angle-based feature clustering for time series
Citation: Aimin Li, Siqi Xiong, Junhuai Li, Saurav Mallik, Yajun Liu, Rong Fei, Hongfang Zhou, Guangming Liu. AngClust: Angle Feature-Based Clustering for Short Time Series Gene Expression Profiles. January 2022. IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM. DOI: 10.1109/TCBB.2022.3192306 Full text: https://ieeexplore.ieee.org/document/9833353/ https://pubmed.ncbi.nlm.nih.gov/35853049/ Highlights * We proposed a novel clustering algorithm based... -
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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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rem
REM - Regression models based on expectation maximization algorithm
This project implements regression models based on expectation maximization (EM) algorithms in case of missing data -
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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). To evaluate the advantages of our newly developed algorithm, we compared the performance of both 16 public datasets and 2... -
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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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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... -
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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... -
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karkinos
Tumor genotyper for Exome sequence that detects SNV,CNV, aTumor purity
karkinos is tumor genotyper which detects single nucleotide variation (SNV), integer copy number variation (CNV) and calculates tumor cellularity from tumor-normal paired sequencing data. Accurate CNV calling is achieved using continuous wavelet analysis and multi-state HMM, while SNV call is adjusted by tumor cellularity and filtered by heuristic filtering algorithm and Fisher Test. Also, Noise calls in low depth region are removed using EM algorithm. -
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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...
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MITSU
Stochastic EM for transcription factor binding site motif discovery
MITSU is an algorithm for discovery of transcription factor binding site (TFBS) motifs. It is based on the stochastic EM (sEM) algorithm, which overcomes some of the limitations of deterministic EM-based algorithms for motif discovery. Unlike previous sEM algorithms for motif discovery, MITSU is unconstrained with regard to the distribution of motif occurrences within the input dataset. MITSU also has the ability to automatically determine the most likely motif width by incorporating... -
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Machine learning library that performs several clustering algorithms (k-means, incremental k-means, DBSCAN, incremental DBSCAN, mitosis, incremental mitosis, mean shift and SHC) and performs several semi-supervised machine learning approaches (self-learning and co-training). --------------------------------------------------------------------------- To run the library, just double click on the jar file. Also, you can use the following command line: Java -Xms1500m -jar "ML Library.jar...
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sdEM
Stochastic Discriminative Expectation Maximization (sdEM)
Stochastic discriminative EM (sdEM) is an online-EM-type algorithm for discriminative training of probabilistic generative models belonging to the natural exponential family. In this work, we introduce and justify this algorithm as a stochastic natural gradient descent method, i.e. a method which accounts for the information geometry in the parameter space of the statistical model. We show how this learning algorithm can be used to train probabilistic generative models by minimizing different... -
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ALCHEMY is a genotype calling algorithm for Affymetrix and Illumina products which is not based on clustering methods. Features include explicit handling of reduced heterozygosity due to inbreeding and accurate results with small sample sizes
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SynergyTwo is ortholog clustering software for both prokaryotic and eukaryotic genomes. It requires Workflow (also available on sourceforge) to manage the computes. It is a reimplementation of the algorithm described in Wapinski et al Bioinformatics 2007.
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Unsupervised TXT classifier
Classify any two TXT documents, no training required - JAVA
This program is made to address two most common issues with the known classifying algorithms. First, over-training and second, shortage of data for a training of categories. Instead, each TXT file is a category on its own, rather than an assigned category. In a way, this is similar to clustering but not really a clustering algorithm since there is some training involved. The summarizer from Classifier4J has been adjusted to accept two inputs (lets call them A and B). Then, the summarizer gets... -
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