Search Results for "em clustering algorithm" - Page 2
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Showing 67 open source projects for "em clustering algorithm"
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Unsupervised Random Forest
On-line Unsupervised Random Forest
This tool uses Random Forest and PAM to cluster observations and to calculate the dissimilarity between observations. 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... -
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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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... of charged "trapped" particles under the influence of EM Fields and there mutual Coulomb interaction. Simbuca is easy to read and understand and can be tweaked to your personal needs. It has been applied to simulate different types of particles: for example antiprotons, positrons, 39K+ , highly charged ions, negatively charged particles. Simbuca also has been applied to simulate Penning/Paul traps, Mr-TOFs, RFQs, ... More info on the wiki: https://sourceforge.net/p/simbuca/wiki
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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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ClusterMX
The ClusterMX program implements various clustering algorithms
The ClusterMX program implements various clustering algorithms including 1) K-Means clustering optimized by random walks; 2) Weighted K-Means (applying force filed to the multidimensional clustering space); 3) EM Clustering Algorithm; 4) Multi-Model Mean Shift Clustering with Random Sampling; 5) Unsupervised K-Wishart clustering. -
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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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VneCplus
虚拟网络映射的仿真器框架
VneCplus是一个虚拟网络映射的仿真器框架，其中实现了NC_DSBA算法，极大地提高了虚拟网络的映射性能。 NC_DSBA（Nodes Clustering and Dynamic Service Balance Awareness Algorithm）虚拟网络映射算法采用节点聚类的策略将虚拟网络请求分割为规模较小的子请求，分而治之，极大地降低了计算复杂度。采用PageRank计算每个节点的负载均衡能力和需求，从而使得虚拟网络请求的映射更趋于均衡，显著提高了虚拟网络映射的收益开销比和承载能力。 -
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QuasiRecomb
Probabilistic inference of viral Quasispecies
... parameters by analysing next generation sequencing data. We offer an implementation of the EM algorithm to find maximum a posteriori estimates of the model parameters and a method to estimate the distribution of viral strains in the quasispecies. The model is validated on simulated data, showing the advantage of explicitly taking the recombination process into account, and tested by applying to reads obtained from experimental HIV samples. -
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qure
software for viral quasispecies reconstruction from next-gen seq. data
QuRe is a program for viral quasispecies reconstruction, specifically developed to analyze long read (>100 bp) NGS data. The software performs alignments of sequence fragments against a reference genome, finds an optimal division of the genome into sliding windows based on coverage and diversity and attempts to reconstruct all the individual sequences of the viral quasispecies--along with their prevalence--using a heuristic algorithm, which matches multinomial distributions of distinct viral... -
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Enhanced Stable Election Protocol- SEP-E
An enhanced stable election protocol for wireless sensor network
While wireless sensor networks are increasingly equipped to handle more complex functions, in-network processing may require these battery powered sensors to judiciously use their constrained energy to prolong the effective network life time especially in a heterogeneous settings. Clustered techniques have since been employed to optimize energy consumption in this energy constrained wireless sensor networks. We propose an Enhanced-SEP clustering algorithm in a three-tier node scenario... -
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GSP program are based on Bayesian framework with an EM algorithm to predict genome size iteratively, which is elegant in mathematics. The model first develop under the no sequencing error model, then extend to the sequencing errors containing model.
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NeuralGas
Self-organized learning
A collection of algorithms based on the topology preserving Neural Gas algorithm for density estimation/quantization/clustering/self-organized learning. I moved this project to GitHub: https://github.com/sergioroa/neuralgas -
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Stanford Machine Learning Course
machine learning course programming exercise
The Stanford Machine Learning Course Exercises repository contains programming assignments from the well-known Stanford Machine Learning online course. It includes implementations of a variety of fundamental algorithms using Python and MATLAB/Octave. The repository covers a broad set of topics such as linear regression, logistic regression, neural networks, clustering, support vector machines, and recommender systems. Each folder corresponds to a specific algorithm or concept, making it easy... -
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EToS (Efficient Technology of Spike sorting; Extracellular recording To Spike trains) is an open-source system for spike sorting. EToS contains the programs of spike detection, feature extraction and clustering.
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EGA
A novel and effictive GA algorithm to solve optimization problem
Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems. To address this problem, we present an efficient genetic algorithm by the combination with clustering methods. The high efficiency of the proposed method results from the fitness estimation and the schema discovery of partial individuals in current population and. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments... -
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K-link
Read clustering algorithm which identifies and removes chimeric reads
This project was developed for ESTs. However, there is no reason it cannot be extended for use on amplicon sequences from 454. It is used in conjunction with WCDest clustering software. -
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Leark is a Data Mining library developed in C#.NET. It contains several methods for ranking web documents described with a set of normalized features, and a feature selection algorithm. The methods are based on perceptron and clustering.
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PocketAnalyzerPCA combines a geometric algorithm for detecting pockets in proteins with Principal Component Analysis and clustering. This enables visualization and analysis of pocket conformational distributions of large sets of protein structures.
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This little software is the realization of EM algorithm in the application of tossiing the coin, which is described in the paper of Michael Collins in 1997.
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BorderFlow implements a general-purpose graph clustering algorithm. It maximizes the inner to outer flow ratio from the border of each cluster to the rest of the graph.
