Orange
2 нояб. 2017 г. 1. Data Mining технология не может заменить аналитика. 2. Технология не может дать ответы на те вопросы которые не были заданы. 3.
Orange: Data Mining Toolbox in Python
Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part
My experience with PostgreSQL and Orange in data mining
data = Orange.data.Table("voting") classifier = Orange.classification.LogisticRegressionLearner(data) c_values = data.domain.class_var.values for d in data[5:8]:.
УДК 004.8 Пальмов С.В. к.т.н. доцент кафедра
Keywords: Orange Data Mining
data mining как инструмент мультимодальной бизнес-аналитики
As Data Mining tools the author used Workflow-model on the on-line platform for analysis and data visualization Orange Data Mining 3.3.2. The research
Zupan Demsar: Introduction to Data Mining
Orange comes with a basic set of widgets for data input preprocessing
Презентация PowerPoint
9 февр. 2018 г. В дальнейшем использовать пакет Orange. Данный пакет можно скачать по адресу: https://orange.biolab.si/. Лаборатория интернет-исследований ...
Интерактивный DataMining
3 апр. 2019 г. Orange: Data Mining Fruitful and Fun. Данный продукт предлагает машинное обучение с открытым исходным кодом и визуализация данных для ...
Orange: Data Mining Toolbox in Python
Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part
Orange Data Mining - k-Means
Orange Data Mining - k-Means. Pagina 1 di 7 https://orange.biolab.si/widget-catalog/unsupervised/kmeans/ k-Means. Groups items using the k-Means clustering
Orange: Data Mining Toolbox in Python
Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part
Orange Data Mining as a tool to compare Classification Algorithms
In this research we choose Orange as data mining tool to classify two types of Key words: Data mining Orange mining tool
Orange Data Mining Library Documentation
Orange Data Mining Library Documentation Release 3. Orange's objects often behave like Python lists and dictionaries
Introduction to Data Mining
Orange comes with a basic set of widgets for data input preprocessing
Orange Software Usage in Data Mining Classification Method on
Orange Software Usage in Data Mining. Classification Method on The Dataset Lenses. To cite this article: Aulia Ishak et al 2020 IOP Conf. Ser.: Mater. Sci.
Orange Data Mining Library Documentation
1 avr. 2022 This is a gentle introduction on scripting in Orange a Python 3 data mining library. We here assume you have already.
Orange: Data Mining Toolbox in Python
Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part
Analysis of Heart Disease using in Data Mining Tools Orange and
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Data mining for the study of the Epidemic (SARS-CoV-2) COVID-19
26 juil. 2020 software for data mining Orange version 3.26.0 in which the algorithm for the analysis of information is filtered to present the current ...
An overview of free software tools for general data mining
six most used free software tools for general data mining that are available today: RapidMiner R
Orange Tweet Analysis Tutorial - hcommonsorg
Orange is an open-source data mining and analysis tool that uses widgets to create workflows to process the data We will be using the basic functionality of the program along with the Text add-on Orange is available for Windows Mac and Linux and the installation is straightforward Navigate to: https://orange biolab si/download
Introduction to Data Mining - filebiolabsi
Orange installation Orange can read data from spreadsheet ?le formats which include tab and comma separated and Excel ?les Let us prepare a data set (with school subjects and grades) in Excel and save it on a local disk In Orange we can use the File widget to load this data Looks ok Orange has correctly guessed that student names are
Introduction to Data Analysis with Orange - hcommonsorg
Orange Data mining toolset (text images networks etc ) Workflow process Widget-based No programming is necessary You create workflows by connecting widgets If you have not already installed Orange and the Text Add-on please see the detailed directions in the PDF Pre-Processing Your Data
What is orange Data Mining Tool? - AskingLotcom
We will use Orange to construct visual data mining ?ows Many similar data mining environments exist but the organizers prefer Orange for a simple reason—they are its authors # If you haven’t already installed Orange please follow the installation guide at http://biolab github io/functional-genomics-workshop-orange #! 1 Data Mining
Comparative Study of Different Orange Data Mining - Springer
Orange data mining tool 1 Introduction Nowadays image classi?cation has taken the front position in different areas of research such as data mining computer vision medical image analysis arti?cial intelligence and so on [1] Figure 1 shows the rapid rise in the unstructured data S Mohapatra (&)
Searches related to orange data mining filetype:pdf
Orange (http://orange biolab si) is a general-purpose machine learning and data mining tool Its multi-layer architecture is suitable for different kinds of users from data mining
[PDF] Introduction to Data Mining
Welcome to the course on Introduction to Data Mining! You will see how common data mining tasks can be accomplished without programming We will use Orange
Import Documents - Orange Data Mining
Import Documents widget retrieves text files from folders and creates a corpus The widget reads txt docx odt pdf xml and conllu files If a folder
Documentation - Orange Data Mining
Orange Data Mining Toolbox Python Library Tutorial Reference Orange 2 7 documentation Support For a list of frequently asked questions see FAQ
(PDF) Orange: Data mining fruitful and fun - A historical perspective
PDF Orange (http://orange biolab si) is a general-purpose machine learning and data mining tool Its multilayer architecture is suitable for different
[PDF] Introduction to Data Analysis with Orange - Humanities Commons
Tweet Analysis Tutorial” PDF available in Google Classroom Orange 3 • Data mining toolset (text images networks etc ) • Workflow process
[PDF] Orange: Data Mining Toolbox in Python - CiteSeerX
The library is designed to simplify the assembly of data analysis workflows and crafting of data mining approaches from a combination of existing components
[PDF] Use of Orange Data Mining Toolbox for Data Analysis in Clinical
This study aims to estimate the HbA1c value with high accuracy Follow-up data of diabetic patients were used as data The Orange data mining software is used
A Fruitful Data Mining Using Orange - Academiaedu
This paper's demonstrations combine music21 with the data mining toolkits Orange and Weka to distinguish works by Monteverdi from works by Bach and German
[PDF] Orange Data Mining as a tool to compare Classification Algorithms
In this research we choose Orange as data mining tool to classify two types of selected medical data for testing (Breast cancer and heart-disease) depending on
[PDF] Orange: data mining toolbox in python - Semantic Scholar
Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming which features interactive data
What is orange data mining tool?
- What is orange Data Mining Tool? What is orange Data Mining Tool? Orange is an open source data visualization and analysis tool, where data mining is done through visual programming or Python scripting. The tool has components for machine learning, add-ons for bioinformatics and text mining and it is packed with features for data analytics.
What is orange add-on for text mining?
- Orange add-on for text mining. It provides access to publicly available data, like NY Times, Twitter and PubMed. Further, it provides tools for preprocessing, constructing vector spaces (like bag-of-words, topic modeling and word2vec) and visualizations like word cloud end geo map.
What is Oracle Data Mining?
- Oracle Data Mining provides GUI, PL/SQL-interface, and JDM-conforming Java interface to methods such as attribute importance, Bayesian classification, association rules, clustering, SVMs, decision trees, and more. www.oracle.com/technology/products/bi/odm/index.html
What are the trends in data mining?
- Data mining trends include further efforts toward the exploration of new application areas; improved scalable, interactive, and constraint-based mining methods; the integration of data mining with web service, database, warehousing, and cloud computing systems; and mining social and information networks.
Orange: Data Mining Toolbox in Python
Janez Dem
sarJANEZ.DEMSAR@FRI.UNI-LJ.SI Toma z CurkTOMAZ.CURK@FRI.UNI-LJ.SI Ale s ErjavecALES.ERJAVE@FRI.UNI-LJ.SICrt GorupCRT.GORUP@FRI.UNI-LJ.SI
Toma z HocevarTOMAZ.HOCEVAR@FRI.UNI-LJ.SIMitar Milutinovi
cMITAR.MILUTINOVIC@FRI.UNI-LJ.SIMartin Mo
zinaMARTIN.MOZINA@FRI.UNI-LJ.SIMatija PolajnarMATIJA.POLAJNAR@FRI.UNI-LJ.SI
Marko ToplakMARKO.TOPLAK@FRI.UNI-LJ.SI
An ze StaricANZE.STARIC@FRI.UNI-LJ.SI MihaStajdoharMIHA.STAJDOHAR@FRI.UNI-LJ.SI
Lan UmekLAN.UMEK@FRI.UNI-LJ.SI
LanZagarLAN.ZAGAR@FRI.UNI-LJ.SI
JureZbontarJURE.ZBONTAR@FRI.UNI-LJ.SI
Marinka
ZitnikMARINKA.ZITNIK@FRI.UNI-LJ.SI
Bla z ZupanBLAZ.ZUPAN@FRI.UNI-LJ.SIFaculty of Computer and Information Science
University of Ljubljana
Tr zaska 25, SI-1000 Ljubljana, SloveniaAbstract
Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part, which features interactive data analysis and component-based assembly of data mining procedures. In the selection and design ofcomponents, we focus on the flexibility of their reuse: our principal intention is to let the user write
simple and clear scripts in Python, which build upon C++implementations of computationally- intensive tasks. Orange is intended both for experienced users and programmers, as well as for students of data mining. Keywords:Python, data mining, machine learning, toolbox, scripting1. Introduction
Scripting languages have recently risen in popularity in all fields of computerscience. Within thecontext of explorative data analysis, they offer advantages like interactivity and fast prototyping by
gluing together existing components or adapting them for new tasks. Python isa scripting language with clear and simple syntax, which also made it popular in education. Its relatively slow execution can be circumvented by using libraries that implement the computationally intensive tasks in low- level languages. Python offers a huge number of extension libraries. Many are related to machine learning,including several general packages like scikit-learn (Pedregosa et al., 2011), PyBrain (Schaul et al.,
2010) and mlpy (Albanese et al., 2012). Orange was conceived in late 1990s and is among the oldest
of such tools. It focuses on simplicity, interactivity through scripting, and component-based design.
c?2013Janez Demsar, Tomaz Curk, Ales Erjavec,Crt Gorup, Tomaz Hocevar, Mitar Milutinovic, Martin Mozina, Matija Polajnar,
Marko Toplak, An
ze Staric, MihaStajdohar, Lan Umek, LanZagar, JureZbontar, MarinkaZitnik and Blaz ZupanDEMSAR, CURK, ERJAVEC ET AL.
2. Toolbox Overview
Orange library is a hierarchically-organized toolbox of data mining components. The low-level procedures at the bottom of the hierarchy, like data filtering, probability assessment and featurescoring, are assembled into higher-level algorithms, such as classificationtree learning. This allows
developers to easily add new functionality at any level and fuse it with the existing code. The main branches of the component hierarchy are: data management and preprocessingfor data input and output, data filtering and sampling, im- putation, featuremanipulation(discretization, continuization, normalization, scalingandscor- ing), and feature selection, classificationwithimplementationsofvarioussupervisedmachinelearningalgorithms(trees, forests, instance-based and Bayesian approaches, rule induction), borrowing from some well-known external libraries such as LIBSVM (Chang and Lin, 2011), regressionincluding linear and lasso regression, partial least square regression, regression trees and forests, and multivariate regression splines, associationfor association rules and frequent itemsets mining, ensemblesimplemented as wrappers for bagging, boosting, forest trees, and stacking, clustering,which includesk-means and hierarchical clustering approaches, evaluationwith cross-validation and other sampling-based procedures, functions for scoring the quality of prediction methods, and procedures for reliability estimation, projectionswith implementations of principal component analysis, multi-dimensional scaling and self-organizing maps. The library is designed to simplify the assembly of data analysis workflows andcrafting of data mining approaches from a combination of existing components. Besides broader range of features, Orange differs from most other Python-based machine learning librariesby its maturity (over 15 years of active development and use), a large user community supportedthrough an active forum, andextensivedocumentationthatincludestutorials, scriptingexamples, datasetrepository, anddoc- umentation for developers. Orange scripting library is also a foundation for its visual programming platform with graphical user interface components for interactive data visualization. The two major packages that are similar to Orange and are still actively developed are scikit- learn (Pedregosa et al., 2011) and mlpy (Albanese et al., 2012). Both are more tightly integrated with numpy and at present better blend into Python"s numerical computing habitat. Orange was on the other hand inspired by classical machine learning that focuses on symbolic methods. Rather than supporting only numerical arrays, Orange data structures combine symbolic, string and numerical attributes and meta data information. User can for instance refer to variablesand values by their names. Variables store mapping functions, a mechanism which for instance allows classifiers to define transformations on training data that are then automatically applied whenmaking predictions. These features also make Orange more suitable for interactive, explorative data analysis. 2350ORANGE: DATAMININGTOOLBOX INPYTHON
3. Scripting Examples
Let us illustrate the utility of Orange through an example of data analysis in Python shell: >>> import Orange >>> data = Orange.data.Table("titanic") >>> len(data) 2201>>> nbc = Orange.classification.bayes.NaiveLearner() >>> svm = Orange.classification.svm.SVMLearner() >>> stack = Orange.ensemble.stacking.StackedClassificationLearner([nbc,svm]) >>> res = Orange.evaluation.testing.cross_validation([nbc, svm, stack], data) >>> Orange.evaluation.scoring.AUC(res) [0.7148500435874006, 0.731873352343742, 0.7635593576372478] We first read the data on survival of 2,201 passengers from HMS Titanic and construct a set of learning algorithms: a naive Bayesian and SVM learner, and a stackedcombination of the two (Wolpert, 1992). We then cross-validate the learners and report the area under ROC curves. Running stacking on the subset of about 470 female passengers improves AUC score: >>> females = Orange.data.Table([d for d in data if d["sex"]=="female"]) >>> len(females) 470
>>> res = Orange.evaluation.testing.cross_validation([stack], females) >>> Orange.evaluation.scoring.AUC(res) [0.8124014221073045] We can use existing machine learning components to craft new ones. For instance, learning algorithms must implement a calloperator that accepts the training data and, optionally, data instance weights, and has to return a model. The following example defines a new learner that
encloses another learner into a feature selection wrapper: it sorts the features by their information
gain (as implemented in Orange.feature.scoring.InfoGain), constructs a new data set with only the mbest features and calls thebaselearner. class FSSLearner(Orange.classification.PyLearner): def __init__(self, base_learner, m=5): self.m = m self.base_learner = base_learner def __call__(self, data, weights=None): gain = Orange.feature.scoring.InfoGain() best = sorted(data.domain.features, key=lambda x: -gain(x, data))[:self.m] domain = Orange.data.Domain(best + [data.domain.class_var]) new_data = Orange.data.Table(domain, data) model = self.base_learner(new_data, weights) return Orange.classification.PyClassifier(classifier=model) Below we compare the original and wrapped naive Bayesian classifier ona data set with 106 instances and 57 features: >>> data = Orange.data.Table("promoters") >>> len(data), len(data.domain.features) (106, 57) 2351DEMSAR, CURK, ERJAVEC ET AL.
>>> bayes = Orange.classification.bayes.NaiveLearner() >>> res = Orange.evaluation.testing.cross_validation([bayes, FSSLearner(bayes)], data) >>> Orange.evaluation.scoring.AUC(res) [0.9329999999999998, 0.945]4. Code Design
Orange"s core is a collection of nearly 200 C++classes that cover the basic data structures and majority of preprocessing and modeling algorithms. The C++part is self-contained, without any calls to Python that would induce unnecessary overhead. The core includes several open source libraries, including LIBSVM (Chang and Lin, 2011), LIBLINEAR (Rong-En et al., 2008), Earth (see http://www.milbo.users.sonic.net/earth), QHull (Barber et al., 1996) and a subset of BLAS (Blackford et al., 2002). The Python layer also uses popular Python libraries numpy for linear algebra, networkx (Hagberg et al., 2008) for working with networks and matplotlib (Hunter,2007) for basic visualization.
The upper layer of Orange is written in Python and includes procedures that are not time-critical. This is also the place at which users outside the core development group most easily contribute to the project. Automated testing of the system relies on over 1,500 regression tests that aremostly based on code snippets from extensive documentation. A part of the code is also covered with stricter unit tests.5. Availability, Requirements and Plans for the Future
Orange is free software released under GPL. The code is hosted on Bitbucket repository ( https:// bitbucket.org/biolab/orange ). Orange runs on Windows, Mac OS X and Linux, and can also be installed from the Python Package Index repository ( pip install Orange). Binary installer for Windows and application bundle for Mac OS X are available on project"sweb site ( http: //orange.biolab.si Orange currently runs on Python 2.6 and 2.7. A version for Python 3 andhigher is under development. There, we will switch to numpy-based data structures and scrap the C++core in favor of using routines from numpy and scipy (Jones et al., 2001-), scikit-learn (Pedregosa et al.,2011) and similar libraries that did not exist when Orange was first conceived. Despite planned
changes in the core, we will maintain backward compatibility. For existing users, the changes of thePython interface will be minor.
Acknowledgments
We would like to acknowledge support for this project from the Slovenian Research Agency (P2-0209, J2-9699, L2-1112), National Institute of Health (P01-HD39691), and Astra Zeneca. We thank
the anonymous reviewers for their constructive comments. 2352ORANGE: DATAMININGTOOLBOX INPYTHON
References
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L. S. Blackford, A. Petitet, R. Pozo, K. Remington, R. C. Whaley, J. Demmel,J. Dongarra, I. Duff, S. Hammarling, and G. Henry. An updated set of basic linear algebra subprograms (BLAS).ACM Transactions on Mathematical Software, 28(2):135-151, 2002. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines.ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011. Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart. Exploring network structure, dynamics, and function using NetworkX. InProceedings of the 7th Python in Science Conference (SciPy2008), pages 11-15, Pasadena, CA USA, 2008. J. D. Hunter. Matplotlib: A 2D graphics environment.Computing In Science & Engineering, 9(3):90-95, 2007.
E. Jones, T. Oliphant, P. Peterson, et al. SciPy: Open source scientifictools for Python, 2001-. URL http://www.scipy.org/. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Pret- tenhofer, R. Weiss, and V. Dubourg. scikit-learn: Machine learning in Python.The Journal ofMachine Learning Research, 12:2825-2830, 2011.
F. Rong-En, C.Kai-Wei, H. Cho-Jui, W. Xiang-Rui, and L. Chih-Jen. LIBLINEAR: A library for large linear classification.Journal of Machine Learning Research, 9:1871-1874, 2008. T. Schaul, J. Bayer, D. Wierstra, Y. Sun, M. Felder, F. Sehnke, T. R¨uckstieß, and J. Schmidhuber.
PyBrain.Journal of Machine Learning Research, 11:743-746, 2010. D. H. Wolpert. Stacked generalization.Neural Networks, 5(2):241-259, 1992. 2353quotesdbs_dbs12.pdfusesText_18
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