ML-Flex: a Flexible Toolbox for Performing Classification Analyses
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KNIME Big Data Workshop
KNIME Big Data Workshop Björn Lohrmann KNIME © 2018 KNIME.com AG. All Rights Reserved. Variety, Velocity, Volume • Variety: – Integrating heterogeneous data… – ... and tools • Velocity: – Real time scoring of millions of records/sec – Continuous data streams – Distributed computation • Volume: – From small files... – ...to distributed data repositories – Moving computation to the data © 2018 KNIME.com AG. All Rights Reserved. 2 2 Variety © 2018 KNIME.com AG. All Rights Reserved. 3 The KNIME Analytics Platform: Open for Every Data, Tool, and User Data Scientist Business Analyst KNIME Analytics Platform External Native External Data Data Access, Analysis, and Legacy Connectors Visualization, and Reporting Tools Distributed / Cloud Execution © 2018 KNIME.com AG. All Rights Reserved. 4 Data Integration © 2018 KNIME.com AG. All Rights Reserved. 5 Integrating R and Python © 2018 KNIME.com AG. All Rights Reserved. 6 Modular Integrations © 2018 KNIME.com AG. All Rights Reserved. 7 Other Programming/Scripting Integrations © 2018 KNIME.com AG. All Rights Reserved. 8 Velocity © 2018 KNIME.com AG. All Rights Reserved. 9 Velocity • High Demand Scoring/Prediction: – Scoring using KNIME Server – Scoring with the KNIME Managed Scoring Service • Continuous Data Streams – Streaming in KNIME © 2018 KNIME.com AG. All Rights Reserved. 10 What is High Demand Scoring? • I have a workflow that takes data, applies an algorithm/model and returns a prediction. • I need to deploy that to hundreds or thousands of end users • I need to update the model/workflow periodically © 2018 KNIME.com AG. All Rights Reserved. 11 How to make a scoring workflow • Use JSON Input/Output nodes • Put workflow on KNIME Server -> REST endpoint for scoring © 2018 KNIME.com AG. -
Feature Selection for Gender Classification in TUIK Life Satisfaction Survey A
Feature Selection for Gender Classification in TUIK Life Satisfaction Survey A. ÇOBAN1 and İ. TARIMER2 1 Muğla Sıtkı Koçman University, Muğla/Turkey, [email protected] 2Muğla Sıtkı Koçman University, Muğla/Turkey, [email protected] Abstract— As known, attribute selection is a in [8] [9] [10]. method that is used before the classification of data Life satisfaction is a cognitive and judicial mining. In this study, a new data set has been created situation which expresses the evaluation of life as a by using attributes expressing overall satisfaction in whole. Happiness on the other hand is conceived as Turkey Statistical Institute (TSI) Life Satisfaction Survey dataset. Attributes are sorted by Ranking an emotional state produced by positive and search method using attribute selection algorithms in negative events and experiences in the life of the a data mining application. These selected attributes individual. Although there are some correlations were subjected to a classification test with Naive between happiness and life satisfaction at different Bayes and Random Forest from machine learning levels, these concepts are still different [11]. algorithms. The feature selection algorithms are The concept of subjective well-being, which we compared according to the number of attributes selected and the classification accuracy rates cannot separate from the concept of happiness, is achievable with them. In this study, which is aimed at defined as people's evaluations of their quality of reducing the dataset volume, the best classification life [6] [7]. Researches and surveys on life result comes up with 3 attributes selected by the Chi2 satisfaction and happiness have been used as algorithm. -
Text Mining Course for KNIME Analytics Platform
Text Mining Course for KNIME Analytics Platform KNIME AG Copyright © 2018 KNIME AG Table of Contents 1. The Open Analytics Platform 2. The Text Processing Extension 3. Importing Text 4. Enrichment 5. Preprocessing 6. Transformation 7. Classification 8. Visualization 9. Clustering 10. Supplementary Workflows Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 2 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ Overview KNIME Analytics Platform Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 3 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ What is KNIME Analytics Platform? • A tool for data analysis, manipulation, visualization, and reporting • Based on the graphical programming paradigm • Provides a diverse array of extensions: • Text Mining • Network Mining • Cheminformatics • Many integrations, such as Java, R, Python, Weka, H2O, etc. Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 4 Noncommercial-Share Alike license 2 https://creativecommons.org/licenses/by-nc-sa/4.0/ Visual KNIME Workflows NODES perform tasks on data Not Configured Configured Outputs Inputs Executed Status Error Nodes are combined to create WORKFLOWS Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 5 Noncommercial-Share Alike license 3 https://creativecommons.org/licenses/by-nc-sa/4.0/ Data Access • Databases • MySQL, MS SQL Server, PostgreSQL • any JDBC (Oracle, DB2, …) • Files • CSV, txt -
The Shogun Machine Learning Toolbox
The Shogun Machine Learning Toolbox Heiko Strathmann, Gatsby Unit, UCL London Open Machine Learning Workshop, MSR, NY August 22, 2014 A bit about Shogun I Open-Source tools for ML problems I Started 1999 by SÖren Sonnenburg & GUNnar Rätsch, made public in 2004 I Currently 8 core-developers + 20 regular contributors I Purely open-source community driven I In Google Summer of Code since 2010 (29 projects!) Ohloh - Summary Ohloh - Code Supervised Learning Given: x y n , want: y ∗ x ∗ I f( i ; i )gi=1 j I Classication: y discrete I Support Vector Machine I Gaussian Processes I Logistic Regression I Decision Trees I Nearest Neighbours I Naive Bayes I Regression: y continuous I Gaussian Processes I Support Vector Regression I (Kernel) Ridge Regression I (Group) LASSO Unsupervised Learning Given: x n , want notion of p x I f i gi=1 ( ) I Clustering: I K-Means I (Gaussian) Mixture Models I Hierarchical clustering I Latent Models I (K) PCA I Latent Discriminant Analysis I Independent Component Analysis I Dimension reduction I (K) Locally Linear Embeddings I Many more... And many more I Multiple Kernel Learning I Structured Output I Metric Learning I Variational Inference I Kernel hypothesis testing I Deep Learning (whooo!) I ... I Bindings to: LibLinear, VowpalWabbit, etc.. http://www.shogun-toolbox.org/page/documentation/ notebook Some Large-Scale Applications I Splice Site prediction: 50m examples of 200m dimensions I Face recognition: 20k examples of 750k dimensions ML in Practice I Modular data represetation I Dense, Sparse, Strings, Streams, ... I Multiple types: 8-128 bit word size I Preprocessing tools I Evaluation I Cross-Validation I Accuracy, ROC, MSE, .. -
Python Libraries, Development Frameworks and Algorithms for Machine Learning Applications
Published by : International Journal of Engineering Research & Technology (IJERT) http://www.ijert.org ISSN: 2278-0181 Vol. 7 Issue 04, April-2018 Python Libraries, Development Frameworks and Algorithms for Machine Learning Applications Dr. V. Hanuman Kumar, Sr. Assoc. Prof., NHCE, Bangalore-560 103 Abstract- Nowadays Machine Learning (ML) used in all sorts numerical values and is of the form y=A0+A1*x, for input of fields like Healthcare, Retail, Travel, Finance, Social Media, value x and A0, A1 are the coefficients used in the etc., ML system is used to learn from input data to construct a representation with the data that we have. The classification suitable model by continuously estimating, optimizing and model helps to identify the sentiment of a particular post. tuning parameters of the model. To attain the stated, For instance a user review can be classified as positive or python programming language is one of the most flexible languages and it does contain special libraries for negative based on the words used in the comments. Emails ML applications namely SciPy, NumPy, etc., which great for can be classified as spam or not, based on these models. The linear algebra and getting to know kernel methods of machine clustering model helps when we are trying to find similar learning. The python programming language is great to use objects. For instance If you are interested in read articles when working with ML algorithms and has easy syntax about chess or basketball, etc.., this model will search for the relatively. When taking the deep-dive into ML, choosing a document with certain high priority words and suggest framework can be daunting. -
Sage for Lattice-Based Cryptography
Sage for Lattice-based Cryptography Martin R. Albrecht and Léo Ducas Oxford Lattice School Outline Sage Lattices Rings Sage Blurb Sage open-source mathematical software system “Creating a viable free open source alternative to Magma, Maple, Mathematica and Matlab.” Sage is a free open-source mathematics software system licensed under the GPL. It combines the power of many existing open-source packages into a common Python-based interface. How to use it command line run sage local webapp run sage -notebook=jupyter hosted webapp https://cloud.sagemath.com 1 widget http://sagecell.sagemath.org 1On SMC you have the choice between “Sage Worksheet” and “Jupyter Notebook”. We recommend the latter. Python & Cython Sage does not come with yet-another ad-hoc mathematical programming language, it uses Python instead. • one of the most widely used programming languages (Google, IML, NASA, Dropbox), • easy for you to define your own data types and methods on it (bitstreams, lattices, cyclotomic rings, :::), • very clean language that results in easy to read code, • a huge number of libraries: statistics, networking, databases, bioinformatic, physics, video games, 3d graphics, numerical computation (SciPy), and pure mathematic • easy to use existing C/C++ libraries from Python (via Cython) Sage =6 Python Sage Python 1/2 1/2 1/2 0 2^3 2^3 8 1 type(2) type(2) <type 'sage.rings.integer.Integer'> <type 'int'> Sage =6 Python Sage Python type(2r) type(2r) <type 'int'> SyntaxError: invalid syntax type(range(10)[0]) type(range(10)[0]) <type 'int'> <type 'int'> -
Effect of Distance Measures on Partitional Clustering Algorithms
Sesham Anand et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (6) , 2015, 5308-5312 Effect of Distance measures on Partitional Clustering Algorithms using Transportation Data Sesham Anand#1, P Padmanabham*2, A Govardhan#3 #1Dept of CSE,M.V.S.R Engg College, Hyderabad, India *2Director, Bharath Group of Institutions, BIET, Hyderabad, India #3Dept of CSE, SIT, JNTU Hyderabad, India Abstract— Similarity/dissimilarity measures in clustering research of algorithms, with an emphasis on unsupervised algorithms play an important role in grouping data and methods in cluster analysis and outlier detection[1]. High finding out how well the data differ with each other. The performance is achieved by using many data index importance of clustering algorithms in transportation data has structures such as the R*-trees.ELKI is designed to be easy been illustrated in previous research. This paper compares the to extend for researchers in data mining particularly in effect of different distance/similarity measures on a partitional clustering algorithm kmedoid(PAM) using transportation clustering domain. ELKI provides a large collection of dataset. A recently developed data mining open source highly parameterizable algorithms, in order to allow easy software ELKI has been used and results illustrated. and fair evaluation and benchmarking of algorithms[1]. Data mining research usually leads to many algorithms Keywords— clustering, transportation Data, partitional for similar kind of tasks. If a comparison is to be made algorithms, cluster validity, distance measures between these algorithms.In ELKI, data mining algorithms and data management tasks are separated and allow for an I. INTRODUCTION independent evaluation. -
An Analysis on the Performance of Rapid Miner and R Programming Language As Data Pre-Processing Tools for Unsupervised Form of Insurance Claim Dataset
International Journal of Computer Sciences and Engineering Open Access Research Paper Vol.-7, Special Issue, 5, March 2019 E-ISSN: 2347-2693 An Analysis on the Performance of Rapid Miner and R Programming Language as Data Pre-processing Tools for Unsupervised Form of Insurance Claim Dataset Surya Susan Thomas1*, Ananthi Sheshasaayee2, 1,2PG & Research Department of Computer Science, Quaid -E- Millath Government College for Women, Chennai, India *Corresponding Author: [email protected], 9940439667 DOI: https://doi.org/10.26438/ijcse/v7si5.14 | Available online at: www.ijcseonline.org Abstract— Data Science has emerged as a super science in almost all the sectors of analytics. Data Mining is the key runner and the pillar stone of data analytics. The analysis and study of any form of data has become so relevant in todays’ scenario and the output from these studies give great societal contributions and hence are of great value. Data analytics involves many steps and one of the primary and the most important one is data pre-processing stage. Raw data has to be cleaned, stabilized and processed to a new form to make the analysis easier and correct. Many pre-processing tools are available but this paper specifically deals with the comparative study of two tools such as Rapid Miner and R programming language which are predominantly used by data analysts. The output of the paper gives an insight into the weightage of the particular tool which can be recommended for better data pre-processing. Keywords- Data analytics, data pre-processing, noise removal, clean data, Rapid Miner, R programming I. -
Tapkee: an Efficient Dimension Reduction Library
JournalofMachineLearningResearch14(2013)2355-2359 Submitted 2/12; Revised 5/13; Published 8/13 Tapkee: An Efficient Dimension Reduction Library Sergey Lisitsyn [email protected] Samara State Aerospace University 34 Moskovskoye shosse 443086 Samara, Russia Christian Widmer∗ [email protected] Computational Biology Center Memorial Sloan-Kettering Cancer Center 415 E 68th street New York City 10065 NY, USA Fernando J. Iglesias Garcia [email protected] KTH Royal Institute of Technology 79 Valhallavagen¨ 10044 Stockholm, Sweden Editor: Cheng Soon Ong Abstract We present Tapkee, a C++ template library that provides efficient implementations of more than 20 widely used dimensionality reduction techniques ranging from Locally Linear Embedding (Roweis and Saul, 2000) and Isomap (de Silva and Tenenbaum, 2002) to the recently introduced Barnes- Hut-SNE (van der Maaten, 2013). Our library was designed with a focus on performance and flexibility. For performance, we combine efficient multi-core algorithms, modern data structures and state-of-the-art low-level libraries. To achieve flexibility, we designed a clean interface for ap- plying methods to user data and provide a callback API that facilitates integration with the library. The library is freely available as open-source software and is distributed under the permissive BSD 3-clause license. We encourage the integration of Tapkee into other open-source toolboxes and libraries. For example, Tapkee has been integrated into the codebase of the Shogun toolbox (Son- nenburg et al., 2010), giving us access to a rich set of kernels, distance measures and bindings to common programming languages including Python, Octave, Matlab, R, Java, C#, Ruby, Perl and Lua. -
User Manual QT
multichannel systems* User Manual QT Information in this document is subject to change without notice. No part of this document may be reproduced or transmitted without the express written permission of Multi Channel Systems MCS GmbH. While every precaution has been taken in the preparation of this document, the publisher and the author assume no responsibility for errors or omissions, or for damages resulting from the use of information contained in this document or from the use of programs and source code that may accompany it. In no event shall the publisher and the author be liable for any loss of profit or any other commercial damage caused or alleged to have been caused directly or indirectly by this document. © 2006-2007 Multi Channel Systems MCS GmbH. All rights reserved. Printed: 2007-01-25 Multi Channel Systems MCS GmbH Aspenhaustraße 21 72770 Reutlingen Germany Fon +49-71 21-90 92 5 - 0 Fax +49-71 21-90 92 5 -11 [email protected] www.multichannelsystems.com Microsoft and Windows are registered trademarks of Microsoft Corporation. Products that are referred to in this document may be either trademarks and/or registered trademarks of their respective holders and should be noted as such. The publisher and the author make no claim to these trademarks. Table Of Contents 1 Introduction 1 1.1 About this Manual 1 2 Important Information and Instructions 3 2.1 Operator's Obligations 3 2.2 Guaranty and Liability 3 2.3 Important Safety Advice 4 2.4 Terms of Use for the program 5 2.5 Limitation of Liability 5 3 First Use 7 3.1 -
A Comparison of Open Source Tools for Data Science
2015 Proceedings of the Conference on Information Systems Applied Research ISSN: 2167-1508 Wilmington, North Carolina USA v8 n3651 __________________________________________________________________________________________________________________________ A Comparison of Open Source Tools for Data Science Hayden Wimmer Department of Information Technology Georgia Southern University Statesboro, GA 30260, USA Loreen M. Powell Department of Information and Technology Management Bloomsburg University Bloomsburg, PA 17815, USA Abstract The next decade of competitive advantage revolves around the ability to make predictions and discover patterns in data. Data science is at the center of this revolution. Data science has been termed the sexiest job of the 21st century. Data science combines data mining, machine learning, and statistical methodologies to extract knowledge and leverage predictions from data. Given the need for data science in organizations, many small or medium organizations are not adequately funded to acquire expensive data science tools. Open source tools may provide the solution to this issue. While studies comparing open source tools for data mining or business intelligence exist, an update on the current state of the art is necessary. This work explores and compares common open source data science tools. Implications include an overview of the state of the art and knowledge for practitioners and academics to select an open source data science tool that suits the requirements of specific data science projects. Keywords: Data Science Tools, Open Source, Business Intelligence, Predictive Analytics, Data Mining. 1. INTRODUCTION Data science is an expensive endeavor. One such example is JMP by SAS. SAS is a primary Data science is an emerging field which provider of data science tools. JMP is one of the intersects data mining, machine learning, more modestly priced tools from SAS with the predictive analytics, statistics, and business price for JMP Pro listed as $14,900. -
A Study of Open Source Data Mining Tools and Its Applications
Research Journal of Applied Sciences, Engineering and Technology 10(10): 1108-1132, 2015 ISSN: 2040-7459; e-ISSN: 2040-7467 © Maxwell Scientific Organization, 2015 Submitted: January 31, 2015 Accepted: March 12, 2015 Published: August 05, 2015 A Study of Open Source Data Mining Tools and its Applications P. Subathra, R. Deepika, K. Yamini, P. Arunprasad and Shriram K. Vasudevan Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham (University), Coimbatore, India Abstract: Data Mining is a technology that is used for the process of analyzing and summarizing useful information from different perspectives of data. The importance of choosing data mining software tools for the developing applications using mining algorithms has led to the analysis of the commercially available open source data mining tools. The study discusses about the following likeness of the data mining tools-KNIME, WEKA, ORANGE, R Tool and Rapid Miner. The Historical Development and state-of-art; (i) The Applications supported, (ii) Data mining algorithms are supported by each tool, (iii) The pre-requisites and the procedures to install a tool, (iv) The input file format supported by each tool. To extract useful information from these data effectively and efficiently, data mining tools are used. The availability of many open source data mining tools, there is an increasing challenge in deciding upon the apace-updated tools for a given application. This study has provided a brief study about the open source knowledge discovery tools with their installation process, algorithms support and input file formats support. Keywords: Data mining, KNIME, ORANGE, Rapid Miner, R-tool, WEKA INTRODUCTION correctness in the classification of the data along with its accuracy.