Search Results for "clustering"
Sort By:
Showing 36 open source projects for "clustering"
View related business solutions-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
-
1
Orange Data Mining
Orange: Interactive data analysis
...Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. ... -
2
Elasticsearch
A Distributed RESTful Search Engine
Elasticsearch is a distributed, RESTful search and analytics engine that lets you store, search and analyze with ease at scale. It lets you perform and combine many types of searches; it scales seamlessly, and offers answers incredibly fast with search results you can rank based on a variety of factors. Elasticsearch can be used for a wide variety of use cases, from maps and metrics to site search and workplace search, and with all data types. -
3
NGSEP
NGSEP (Next Generation Sequencing Experience Platform)
...The current version provides functionalities for both de-novo and reference guided analysis of sequencing data, including genome assembly, read mapping, variants detection and genotyping and de-novo analysis of data generated from reduced representation protocols. NGSEP also provides modules for analysis of genomic variation databases (VCF files), including functional annotation, filtering, format conversion, comparison, clustering, imputation, introgression analysis and different kinds of statistics. Since version 4, we provide functionalities for management of genomes and transcriptomes, including genome alignment and annotation of transposable elements. A complete list of functionalities is available in our wiki (https://sourceforge.net/p/ngsep/wiki/Home/). ... -
4
Faum
Fast Autonomous Unsupervised Multidimiensional Classification
This is the proof-of-concept implementation of the FAUM Clustering method. This implementation was used to perform the published results and is now released in the hope that it will be useful. -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
-
5
DynaQ
Innovative text document search. http://dynaq.opendfki.de for details.
The goal of DynaQ is to develop an inquiry system to explore the personal information space, supporting you with the searching paradigm 'orienteering'. DynaQ is a (desktop)search engine with enhanced functionality for file, email and blog search. Look at our GitLab homepage for sourcecode and documentation: http://dynaq.opendfki.de -
6
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. ... -
7
jLDADMM
A Java package for the LDA and DMM topic models
...It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models. See the usage of jLDADMM in its website at http://jldadmm.sourceforge.net/ -
8
The Java Data Mining Package (JDMP) is a library that provides methods for analyzing data with the help of machine learning algorithms (e.g. clustering, classification, graphical models, neural networks, Bayesian networks, text processing, optimization).
-
9
Adaptive Gaussian Filtering
Machine learning with Gaussian kernels.
...For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation routines. It is written in C++. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
10
Deem
Analyze time-course data with significance tests, clustering, modeling
Use statistical methods to analyze time-course data (gene expression microarray and RNA-seq data in particular, but not limited to). Apply significance tests to filter out only significant genes or time series. Cluster time series into similar groups. Generate network models, including linear or non-linear models. Variable selection and optimization routines included. Written in Scala and R. The application is a cross-platform desktop app with a simple GUI and is fully functional... -
11
Weka4OC GUI for Overlapping clustering
Weka4OC: Weka for Overlapping Clustering is a GUI extending WEKA
This is a GUI application for learning non disjoint groups based on Weka machine learning framework. It offers a variety of learning methods, based on k-means, able to produce overlapping clusters. The application also contains an evaluation framework that calculates several external validation measures. The application offers a visualization tool to discover overlapping groups. -
12
ktree
clustering, machine learning, algorithms
This project has moved to github at http://lmwtree.devries.ninja. -
13
Unsupervised TXT classifier
Classify any two TXT documents, no training required - JAVA
...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 trained with A to summarize a document B, and vice versa. This extracts a relevant structure for both documents (and thus avoids the over-training) which are then compared using the Vector-Space analysis to give a range of belonging of one document to another (and thus avoids the shortage of information). ... -
14
DocCO
Non-disjoint groupping of Documents based on word sequence approach
This is a GUI for learning non disjoint groups of documents based on Weka machine learning framework. It offers the possibility to make non disjoint clustering of documents using both vectorial and sequential representation (word sequence approach based on WSK kernel). All data format supported by WEKA could be used in DocCO. Data could be loaded from files, from databases or from specified URL. All the preprocessing techniques implemented in WEKA could be used before performing the learning. -
15
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.
-
16
Data mining tool for sequences (e.g. trajectories on a map, visited web pages, etc.) that creates a succinct description of the sequences, given a taxonomy (e.g. regions and sub-regions in the map, categories and sub-categories of pages, etc.).
-
17
SLEDRIDE: Simplified Learning about Expression Data Running in a Desktop Environment. To provide a general workbench for pipe-lining microarray gene expression data from supervised learning results into unsupervised learning methods.
-
18
Java package to study a clustering model described in the paper \"Novel Clustering Algorithm Based Upon Games on Evolving Network\" by Q. Li, Z. Chen, Y. He and J-P. Jiang (in arxiv: http://arxiv.org/pdf/0812.5064v1), generalizations and similar issues.
-
19
-
20
SONIVIS:Tool aims at analysing social (virtual) information spaces like Wikis. These spaces are investigated by using different network definitions (collaboration/information networks). Clustering algorithms and statistiscal analyses are provided.
-
21
Clown is a "clustering" framework. It allows you to cluster datasets (in ARFF) format using a number of different clustering algorithms.
-
22
SYRAH si propone di far emergere e rappresentare i concetti espressi per mezzo di un linguaggio naturale. SYRAH aims to discover and represent concepts expressed in natural languages. NLP, lemma, lemmario, italiano, rete, semantica, clustering, semantic
-
23
GoldenOrb is a java library under the Apache License V2.0 for correlation, summarization and clustering of text information.
-
24
The Databionics ESOM Tools offer many data mining tasks using Emergent Self-Organizing Maps. Visualization, clustering, and classification of high-dimensional data using databionics principles can be performed interactively or automatically.
-
25
Machine learning toolkit for unsupervised and semi-supervised clustering that demonstrates excellent results on real-world data (see Bekkerman et al. ICML-2005 and ECML-2006).
