Content-Based Recommendation

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Content-Based Recommendation"

Transcription

1 Content-Based Recommendation

2 Content-based? Item descriptions to identify items that are of particular interest to the user

3 Example

4 Example

5 Comparing with Noncontent based Items User-based CF Searches for similar users in user-item rating matrix No rating Item-feature matrix Ratings

6 Item Representation Structured Unstructured Semi-structured

7 Attribute - value Structured

8 Unstructured Full-text No attributes formally defined Other complicated problems - such as synonymy, polysymy

9 Semi-structured Structured + unstructured Well defined attributes/values + free text

10 Conversion of Unstructured Data Need to convert to structured form IR techniques VSM, TF-IDF, stemming, etc.

11 User Profile A model of user s preference A history of the user s interactions Recently viewed items Already viewed items Training data machine learning

12 User Profile Tear Rate Reduced Normal No Young Age Pre-presb Yes Presbyoptic Prescription Yes Hypermetrope Myope Astigmatic Yes Yes No No

13 User Profiling Collects information about individual user User Modeling side Adaptive System User Model Provides adaptation effect Adaptation side User profiling == user modeling

14 User Profile Customization Manual Limitations user efforts (interest change), no ordering Rule-based recommendation history based rules

15 User Profile

16 User Model Learning Classification problem Classifying to Like or Dislike Training data feedbacks Probability of classification Unstructured data conversion Feature selection high/low dimensions

17 User Model Learning Training Data Target Data Train/Learn Classify/Recommen

18 UM Learning Feedbacks Implicit feedback Indirect interaction Opened document, Reading time, etc. Large data, high uncertainty

19 UM Learning Feedbacks Explicit Directly from users No noise, hard to obtain

20 User Model Learning Feature Selection Problem of high dimensional input vectors Overfit (especially when a dataset is small) Document frequency thresholding, Information gain, Mutual information, Chi square statistic, Term strength

21 Overfitting Overfit Underfit

22 User Model Learning Feature Selection Mutual Information A = number of times t and c co-occur B = number of times t occurs without c C = number of times c occurs without t N = number of total documents I(t,c) = log A! N (A + C)! (A + B)

23 User Model Learning Feature Selection Austrian train fire accident After learning 5 documents Train Fire Alps Austria People A B C

24 Decision Tree Partitioning dataset into trees Ideal for structured, small data Performance, simplicity, understandability ID3 (Iterative Dichotomiser 3)

25 Decision Tree Example Using WEKA Machine learning algorithm package JAVA API, interactive UI

26 Decision Tree Example - dataset Training Testing attribute as inputs attribute to be es

27 Decision Tree Example - tree

28

29 Decision Tree Example - tree Tear Rate Reduced Normal Young Age Pre-presbyoptic es Presbyoptic Prescription Yes Hypermetrope s Myope Astigmatic Yes No

30 Decision Tree Example - evaluation

31 k Nearest Neighbor Prepare training data (classification labels) Extract k most similar items Decide the label of the test data by looking at knn s

32 k Nearest Neighbor Example k=3 k=5

33 Linear Classifier aining data Tries to find out a hyperplane that best separates classes

34 Linear Classifier SVN Support Vector Machine Maximizes the distance between decision boundary & support vector (closest training instance) Avoids overfitting

35 Linear Classifier SVM

36 Naive Bayes VSM lack of theoretical justification Probabilistic text classification method Naive = term independence Probability document d is classified to category c

37 Naive Bayes Multivariate Bernoulli Document probability = of term probability term independence assumption naive

38 Naive Bayes Multinomial Non-binary

39 Naive Bayes Example

40 Conclusions User model learns from content (description, fulltext, etc) itself Implicit, explicit method Classifying like, dislike Limitations not enough content Hybrid content + collaborative

Search Taxonomy. Web Search. Search Engine Optimization. Information Retrieval

Search Taxonomy. Web Search. Search Engine Optimization. Information Retrieval Information Retrieval INFO 4300 / CS 4300! Retrieval models Older models» Boolean retrieval» Vector Space model Probabilistic Models» BM25» Language models Web search» Learning to Rank Search Taxonomy!

More information

Social Media Mining. Data Mining Essentials

Social Media Mining. Data Mining Essentials Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

More information

Machine Learning Logistic Regression

Machine Learning Logistic Regression Machine Learning Logistic Regression Jeff Howbert Introduction to Machine Learning Winter 2012 1 Logistic regression Name is somewhat misleading. Really a technique for classification, not regression.

More information

Linear Threshold Units

Linear Threshold Units Linear Threshold Units w x hx (... w n x n w We assume that each feature x j and each weight w j is a real number (we will relax this later) We will study three different algorithms for learning linear

More information

Email Spam Detection A Machine Learning Approach

Email Spam Detection A Machine Learning Approach Email Spam Detection A Machine Learning Approach Ge Song, Lauren Steimle ABSTRACT Machine learning is a branch of artificial intelligence concerned with the creation and study of systems that can learn

More information

Recommender Systems: Content-based, Knowledge-based, Hybrid. Radek Pelánek

Recommender Systems: Content-based, Knowledge-based, Hybrid. Radek Pelánek Recommender Systems: Content-based, Knowledge-based, Hybrid Radek Pelánek 2015 Today lecture, basic principles: content-based knowledge-based hybrid, choice of approach,... critiquing, explanations,...

More information

Classification algorithm in Data mining: An Overview

Classification algorithm in Data mining: An Overview Classification algorithm in Data mining: An Overview S.Neelamegam #1, Dr.E.Ramaraj *2 #1 M.phil Scholar, Department of Computer Science and Engineering, Alagappa University, Karaikudi. *2 Professor, Department

More information

Logistic Regression. Vibhav Gogate The University of Texas at Dallas. Some Slides from Carlos Guestrin, Luke Zettlemoyer and Dan Weld.

Logistic Regression. Vibhav Gogate The University of Texas at Dallas. Some Slides from Carlos Guestrin, Luke Zettlemoyer and Dan Weld. Logistic Regression Vibhav Gogate The University of Texas at Dallas Some Slides from Carlos Guestrin, Luke Zettlemoyer and Dan Weld. Generative vs. Discriminative Classifiers Want to Learn: h:x Y X features

More information

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not. Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C

More information

An Introduction to Data Mining

An Introduction to Data Mining An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail

More information

Employer Health Insurance Premium Prediction Elliott Lui

Employer Health Insurance Premium Prediction Elliott Lui Employer Health Insurance Premium Prediction Elliott Lui 1 Introduction The US spends 15.2% of its GDP on health care, more than any other country, and the cost of health insurance is rising faster than

More information

Anti-Spam Filter Based on Naïve Bayes, SVM, and KNN model

Anti-Spam Filter Based on Naïve Bayes, SVM, and KNN model AI TERM PROJECT GROUP 14 1 Anti-Spam Filter Based on,, and model Yun-Nung Chen, Che-An Lu, Chao-Yu Huang Abstract spam email filters are a well-known and powerful type of filters. We construct different

More information

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic

More information

Machine Learning using MapReduce

Machine Learning using MapReduce Machine Learning using MapReduce What is Machine Learning Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous

More information

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015 An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content

More information

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical

More information

MACHINE LEARNING IN HIGH ENERGY PHYSICS

MACHINE LEARNING IN HIGH ENERGY PHYSICS MACHINE LEARNING IN HIGH ENERGY PHYSICS LECTURE #1 Alex Rogozhnikov, 2015 INTRO NOTES 4 days two lectures, two practice seminars every day this is introductory track to machine learning kaggle competition!

More information

Machine learning for algo trading

Machine learning for algo trading Machine learning for algo trading An introduction for nonmathematicians Dr. Aly Kassam Overview High level introduction to machine learning A machine learning bestiary What has all this got to do with

More information

Support Vector Machines with Clustering for Training with Very Large Datasets

Support Vector Machines with Clustering for Training with Very Large Datasets Support Vector Machines with Clustering for Training with Very Large Datasets Theodoros Evgeniou Technology Management INSEAD Bd de Constance, Fontainebleau 77300, France theodoros.evgeniou@insead.fr Massimiliano

More information

Machine Learning in Spam Filtering

Machine Learning in Spam Filtering Machine Learning in Spam Filtering A Crash Course in ML Konstantin Tretyakov kt@ut.ee Institute of Computer Science, University of Tartu Overview Spam is Evil ML for Spam Filtering: General Idea, Problems.

More information

Active Learning SVM for Blogs recommendation

Active Learning SVM for Blogs recommendation Active Learning SVM for Blogs recommendation Xin Guan Computer Science, George Mason University Ⅰ.Introduction In the DH Now website, they try to review a big amount of blogs and articles and find the

More information

Supervised Learning (Big Data Analytics)

Supervised Learning (Big Data Analytics) Supervised Learning (Big Data Analytics) Vibhav Gogate Department of Computer Science The University of Texas at Dallas Practical advice Goal of Big Data Analytics Uncover patterns in Data. Can be used

More information

Machine Learning Final Project Spam Email Filtering

Machine Learning Final Project Spam Email Filtering Machine Learning Final Project Spam Email Filtering March 2013 Shahar Yifrah Guy Lev Table of Content 1. OVERVIEW... 3 2. DATASET... 3 2.1 SOURCE... 3 2.2 CREATION OF TRAINING AND TEST SETS... 4 2.3 FEATURE

More information

Bagged Ensemble Classifiers for Sentiment Classification of Movie Reviews

Bagged Ensemble Classifiers for Sentiment Classification of Movie Reviews www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 3 Issue 2 February, 2014 Page No. 3951-3961 Bagged Ensemble Classifiers for Sentiment Classification of Movie

More information

BIDM Project. Predicting the contract type for IT/ITES outsourcing contracts

BIDM Project. Predicting the contract type for IT/ITES outsourcing contracts BIDM Project Predicting the contract type for IT/ITES outsourcing contracts N a n d i n i G o v i n d a r a j a n ( 6 1 2 1 0 5 5 6 ) The authors believe that data modelling can be used to predict if an

More information

Analysis Tools and Libraries for BigData

Analysis Tools and Libraries for BigData + Analysis Tools and Libraries for BigData Lecture 02 Abhijit Bendale + Office Hours 2 n Terry Boult (Waiting to Confirm) n Abhijit Bendale (Tue 2:45 to 4:45 pm). Best if you email me in advance, but I

More information

Knowledge Discovery from patents using KMX Text Analytics

Knowledge Discovery from patents using KMX Text Analytics Knowledge Discovery from patents using KMX Text Analytics Dr. Anton Heijs anton.heijs@treparel.com Treparel Abstract In this white paper we discuss how the KMX technology of Treparel can help searchers

More information

The Need for Training in Big Data: Experiences and Case Studies

The Need for Training in Big Data: Experiences and Case Studies The Need for Training in Big Data: Experiences and Case Studies Guy Lebanon Amazon Background and Disclaimer All opinions are mine; other perspectives are legitimate. Based on my experience as a professor

More information

Data Mining. Nonlinear Classification

Data Mining. Nonlinear Classification Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15

More information

Data Mining Tools. Jean- Gabriel Ganascia LIP6 University Pierre et Marie Curie 4, place Jussieu, 75252 Paris, Cedex 05 Jean- Gabriel.Ganascia@lip6.

Data Mining Tools. Jean- Gabriel Ganascia LIP6 University Pierre et Marie Curie 4, place Jussieu, 75252 Paris, Cedex 05 Jean- Gabriel.Ganascia@lip6. Data Mining Tools Jean- Gabriel Ganascia LIP6 University Pierre et Marie Curie 4, place Jussieu, 75252 Paris, Cedex 05 Jean- Gabriel.Ganascia@lip6.fr DATA BASES Data mining Extraction Data mining Interpretation/

More information

Université de Montpellier 2 Hugo Alatrista-Salas : hugo.alatrista-salas@teledetection.fr

Université de Montpellier 2 Hugo Alatrista-Salas : hugo.alatrista-salas@teledetection.fr Université de Montpellier 2 Hugo Alatrista-Salas : hugo.alatrista-salas@teledetection.fr WEKA Gallirallus Zeland) australis : Endemic bird (New Characteristics Waikato university Weka is a collection

More information

STA 4273H: Statistical Machine Learning

STA 4273H: Statistical Machine Learning STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct

More information

Statistical Machine Learning

Statistical Machine Learning Statistical Machine Learning UoC Stats 37700, Winter quarter Lecture 4: classical linear and quadratic discriminants. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classes

More information

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Ensemble learning Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 8 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Combining multiple models Bagging The basic idea

More information

An Overview of Knowledge Discovery Database and Data mining Techniques

An Overview of Knowledge Discovery Database and Data mining Techniques An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,

More information

Statistical Feature Selection Techniques for Arabic Text Categorization

Statistical Feature Selection Techniques for Arabic Text Categorization Statistical Feature Selection Techniques for Arabic Text Categorization Rehab M. Duwairi Department of Computer Information Systems Jordan University of Science and Technology Irbid 22110 Jordan Tel. +962-2-7201000

More information

Data Mining - Evaluation of Classifiers

Data Mining - Evaluation of Classifiers Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010

More information

Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data

Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data CMPE 59H Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Non-linear

More information

RANDOM PROJECTIONS FOR SEARCH AND MACHINE LEARNING

RANDOM PROJECTIONS FOR SEARCH AND MACHINE LEARNING = + RANDOM PROJECTIONS FOR SEARCH AND MACHINE LEARNING Stefan Savev Berlin Buzzwords June 2015 KEYWORD-BASED SEARCH Document Data 300 unique words per document 300 000 words in vocabulary Data sparsity:

More information

lop Building Machine Learning Systems with Python en source

lop Building Machine Learning Systems with Python en source Building Machine Learning Systems with Python Master the art of machine learning with Python and build effective machine learning systems with this intensive handson guide Willi Richert Luis Pedro Coelho

More information

1. Classification problems

1. Classification problems Neural and Evolutionary Computing. Lab 1: Classification problems Machine Learning test data repository Weka data mining platform Introduction Scilab 1. Classification problems The main aim of a classification

More information

A Proposed Algorithm for Spam Filtering Emails by Hash Table Approach

A Proposed Algorithm for Spam Filtering Emails by Hash Table Approach International Research Journal of Applied and Basic Sciences 2013 Available online at www.irjabs.com ISSN 2251-838X / Vol, 4 (9): 2436-2441 Science Explorer Publications A Proposed Algorithm for Spam Filtering

More information

Machine Learning. Chapter 18, 21. Some material adopted from notes by Chuck Dyer

Machine Learning. Chapter 18, 21. Some material adopted from notes by Chuck Dyer Machine Learning Chapter 18, 21 Some material adopted from notes by Chuck Dyer What is learning? Learning denotes changes in a system that... enable a system to do the same task more efficiently the next

More information

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4.

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4. Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví Pavel Kříž Seminář z aktuárských věd MFF 4. dubna 2014 Summary 1. Application areas of Insurance Analytics 2. Insurance Analytics

More information

Statistical Models in Data Mining

Statistical Models in Data Mining Statistical Models in Data Mining Sargur N. Srihari University at Buffalo The State University of New York Department of Computer Science and Engineering Department of Biostatistics 1 Srihari Flood of

More information

Assessment. Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall

Assessment. Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall Automatic Photo Quality Assessment Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall Estimating i the photorealism of images: Distinguishing i i paintings from photographs h Florin

More information

Local features and matching. Image classification & object localization

Local features and matching. Image classification & object localization Overview Instance level search Local features and matching Efficient visual recognition Image classification & object localization Category recognition Image classification: assigning a class label to

More information

A Survey on Pre-processing and Post-processing Techniques in Data Mining

A Survey on Pre-processing and Post-processing Techniques in Data Mining , pp. 99-128 http://dx.doi.org/10.14257/ijdta.2014.7.4.09 A Survey on Pre-processing and Post-processing Techniques in Data Mining Divya Tomar and Sonali Agarwal Indian Institute of Information Technology,

More information

11 Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial Least Squares Regression

11 Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial Least Squares Regression Frank C Porter and Ilya Narsky: Statistical Analysis Techniques in Particle Physics Chap. c11 2013/9/9 page 221 le-tex 221 11 Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial

More information

Probabilistic Linear Classification: Logistic Regression. Piyush Rai IIT Kanpur

Probabilistic Linear Classification: Logistic Regression. Piyush Rai IIT Kanpur Probabilistic Linear Classification: Logistic Regression Piyush Rai IIT Kanpur Probabilistic Machine Learning (CS772A) Jan 18, 2016 Probabilistic Machine Learning (CS772A) Probabilistic Linear Classification:

More information

Large-scale Data Mining: MapReduce and Beyond Part 2: Algorithms. Spiros Papadimitriou, IBM Research Jimeng Sun, IBM Research Rong Yan, Facebook

Large-scale Data Mining: MapReduce and Beyond Part 2: Algorithms. Spiros Papadimitriou, IBM Research Jimeng Sun, IBM Research Rong Yan, Facebook Large-scale Data Mining: MapReduce and Beyond Part 2: Algorithms Spiros Papadimitriou, IBM Research Jimeng Sun, IBM Research Rong Yan, Facebook Part 2:Mining using MapReduce Mining algorithms using MapReduce

More information

Recommendations in Mobile Environments. Professor Hui Xiong Rutgers Business School Rutgers University. Rutgers, the State University of New Jersey

Recommendations in Mobile Environments. Professor Hui Xiong Rutgers Business School Rutgers University. Rutgers, the State University of New Jersey 1 Recommendations in Mobile Environments Professor Hui Xiong Rutgers Business School Rutgers University ADMA-2014 Rutgers, the State University of New Jersey Big Data 3 Big Data Application Requirements

More information

Projektgruppe. Categorization of text documents via classification

Projektgruppe. Categorization of text documents via classification Projektgruppe Steffen Beringer Categorization of text documents via classification 4. Juni 2010 Content Motivation Text categorization Classification in the machine learning Document indexing Construction

More information

Defending Networks with Incomplete Information: A Machine Learning Approach. Alexandre Pinto alexcp@mlsecproject.org @alexcpsec @MLSecProject

Defending Networks with Incomplete Information: A Machine Learning Approach. Alexandre Pinto alexcp@mlsecproject.org @alexcpsec @MLSecProject Defending Networks with Incomplete Information: A Machine Learning Approach Alexandre Pinto alexcp@mlsecproject.org @alexcpsec @MLSecProject Agenda Security Monitoring: We are doing it wrong Machine Learning

More information

Research on Sentiment Classification of Chinese Micro Blog Based on

Research on Sentiment Classification of Chinese Micro Blog Based on Research on Sentiment Classification of Chinese Micro Blog Based on Machine Learning School of Economics and Management, Shenyang Ligong University, Shenyang, 110159, China E-mail: 8e8@163.com Abstract

More information

Applying Machine Learning to Stock Market Trading Bryce Taylor

Applying Machine Learning to Stock Market Trading Bryce Taylor Applying Machine Learning to Stock Market Trading Bryce Taylor Abstract: In an effort to emulate human investors who read publicly available materials in order to make decisions about their investments,

More information

Data Mining Analytics for Business Intelligence and Decision Support

Data Mining Analytics for Business Intelligence and Decision Support Data Mining Analytics for Business Intelligence and Decision Support Chid Apte, T.J. Watson Research Center, IBM Research Division Knowledge Discovery and Data Mining (KDD) techniques are used for analyzing

More information

Machine Learning. CUNY Graduate Center, Spring 2013. Professor Liang Huang. huang@cs.qc.cuny.edu

Machine Learning. CUNY Graduate Center, Spring 2013. Professor Liang Huang. huang@cs.qc.cuny.edu Machine Learning CUNY Graduate Center, Spring 2013 Professor Liang Huang huang@cs.qc.cuny.edu http://acl.cs.qc.edu/~lhuang/teaching/machine-learning Logistics Lectures M 9:30-11:30 am Room 4419 Personnel

More information

Data Mining Essentials

Data Mining Essentials This chapter is from Social Media Mining: An Introduction. By Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu. Cambridge University Press, 2014. Draft version: April 20, 2014. Complete Draft and Slides

More information

Detection. Perspective. Network Anomaly. Bhattacharyya. Jugal. A Machine Learning »C) Dhruba Kumar. Kumar KaKta. CRC Press J Taylor & Francis Croup

Detection. Perspective. Network Anomaly. Bhattacharyya. Jugal. A Machine Learning »C) Dhruba Kumar. Kumar KaKta. CRC Press J Taylor & Francis Croup Network Anomaly Detection A Machine Learning Perspective Dhruba Kumar Bhattacharyya Jugal Kumar KaKta»C) CRC Press J Taylor & Francis Croup Boca Raton London New York CRC Press is an imprint of the Taylor

More information

Data Mining for Customer Service Support. Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin

Data Mining for Customer Service Support. Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin Data Mining for Customer Service Support Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin Traditional Hotline Services Problem Traditional Customer Service Support (manufacturing)

More information

Analytics on Big Data

Analytics on Big Data Analytics on Big Data Riccardo Torlone Università Roma Tre Credits: Mohamed Eltabakh (WPI) Analytics The discovery and communication of meaningful patterns in data (Wikipedia) It relies on data analysis

More information

Lecture 6: Logistic Regression

Lecture 6: Logistic Regression Lecture 6: CS 194-10, Fall 2011 Laurent El Ghaoui EECS Department UC Berkeley September 13, 2011 Outline Outline Classification task Data : X = [x 1,..., x m]: a n m matrix of data points in R n. y { 1,

More information

Classification and Prediction techniques using Machine Learning for Anomaly Detection.

Classification and Prediction techniques using Machine Learning for Anomaly Detection. Classification and Prediction techniques using Machine Learning for Anomaly Detection. Pradeep Pundir, Dr.Virendra Gomanse,Narahari Krishnamacharya. *( Department of Computer Engineering, Jagdishprasad

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015

International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015 RESEARCH ARTICLE OPEN ACCESS Data Mining Technology for Efficient Network Security Management Ankit Naik [1], S.W. Ahmad [2] Student [1], Assistant Professor [2] Department of Computer Science and Engineering

More information

Bayesian Machine Learning (ML): Modeling And Inference in Big Data. Zhuhua Cai Google, Rice University caizhua@gmail.com

Bayesian Machine Learning (ML): Modeling And Inference in Big Data. Zhuhua Cai Google, Rice University caizhua@gmail.com Bayesian Machine Learning (ML): Modeling And Inference in Big Data Zhuhua Cai Google Rice University caizhua@gmail.com 1 Syllabus Bayesian ML Concepts (Today) Bayesian ML on MapReduce (Next morning) Bayesian

More information

W. Heath Rushing Adsurgo LLC. Harness the Power of Text Analytics: Unstructured Data Analysis for Healthcare. Session H-1 JTCC: October 23, 2015

W. Heath Rushing Adsurgo LLC. Harness the Power of Text Analytics: Unstructured Data Analysis for Healthcare. Session H-1 JTCC: October 23, 2015 W. Heath Rushing Adsurgo LLC Harness the Power of Text Analytics: Unstructured Data Analysis for Healthcare Session H-1 JTCC: October 23, 2015 Outline Demonstration: Recent article on cnn.com Introduction

More information

Improving spam mail filtering using classification algorithms with discretization Filter

Improving spam mail filtering using classification algorithms with discretization Filter International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) International Journal of Emerging Technologies in Computational

More information

Data Mining Techniques for Prognosis in Pancreatic Cancer

Data Mining Techniques for Prognosis in Pancreatic Cancer Data Mining Techniques for Prognosis in Pancreatic Cancer by Stuart Floyd A Thesis Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUE In partial fulfillment of the requirements for the Degree

More information

Data Mining. Cluster Analysis: Advanced Concepts and Algorithms

Data Mining. Cluster Analysis: Advanced Concepts and Algorithms Data Mining Cluster Analysis: Advanced Concepts and Algorithms Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 More Clustering Methods Prototype-based clustering Density-based clustering Graph-based

More information

Machine Learning and Pattern Recognition Logistic Regression

Machine Learning and Pattern Recognition Logistic Regression Machine Learning and Pattern Recognition Logistic Regression Course Lecturer:Amos J Storkey Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh Crichton Street,

More information

The Optimality of Naive Bayes

The Optimality of Naive Bayes The Optimality of Naive Bayes Harry Zhang Faculty of Computer Science University of New Brunswick Fredericton, New Brunswick, Canada email: hzhang@unbca E3B 5A3 Abstract Naive Bayes is one of the most

More information

Bayes and Naïve Bayes. cs534-machine Learning

Bayes and Naïve Bayes. cs534-machine Learning Bayes and aïve Bayes cs534-machine Learning Bayes Classifier Generative model learns Prediction is made by and where This is often referred to as the Bayes Classifier, because of the use of the Bayes rule

More information

Big Data Analytics and Optimization

Big Data Analytics and Optimization Big Data Analytics and Optimization C e r t i f i c a t e P r o g r a m i n E n g i n e e r i n g E x c e l l e n c e C e r t i f i c a t e P r o g r a m s i n A c c e l e r a t e d E n g i n e e r i n

More information

A semi-supervised Spam mail detector

A semi-supervised Spam mail detector A semi-supervised Spam mail detector Bernhard Pfahringer Department of Computer Science, University of Waikato, Hamilton, New Zealand Abstract. This document describes a novel semi-supervised approach

More information

An Analysis of Factors Used in Search Engine Ranking

An Analysis of Factors Used in Search Engine Ranking An Analysis of Factors Used in Search Engine Ranking Albert Bifet 1 Carlos Castillo 2 Paul-Alexandru Chirita 3 Ingmar Weber 4 1 Technical University of Catalonia 2 University of Chile 3 L3S Research Center

More information

OPINION MINING IN PRODUCT REVIEW SYSTEM USING BIG DATA TECHNOLOGY HADOOP

OPINION MINING IN PRODUCT REVIEW SYSTEM USING BIG DATA TECHNOLOGY HADOOP OPINION MINING IN PRODUCT REVIEW SYSTEM USING BIG DATA TECHNOLOGY HADOOP 1 KALYANKUMAR B WADDAR, 2 K SRINIVASA 1 P G Student, S.I.T Tumkur, 2 Assistant Professor S.I.T Tumkur Abstract- Product Review System

More information

Identification algorithms for hybrid systems

Identification algorithms for hybrid systems Identification algorithms for hybrid systems Giancarlo Ferrari-Trecate Modeling paradigms Chemistry White box Thermodynamics System Mechanics... Drawbacks: Parameter values of components must be known

More information

Forecasting stock markets with Twitter

Forecasting stock markets with Twitter Forecasting stock markets with Twitter Argimiro Arratia argimiro@lsi.upc.edu Joint work with Marta Arias and Ramón Xuriguera To appear in: ACM Transactions on Intelligent Systems and Technology, 2013,

More information

Towards Effective Recommendation of Social Data across Social Networking Sites

Towards Effective Recommendation of Social Data across Social Networking Sites Towards Effective Recommendation of Social Data across Social Networking Sites Yuan Wang 1,JieZhang 2, and Julita Vassileva 1 1 Department of Computer Science, University of Saskatchewan, Canada {yuw193,jiv}@cs.usask.ca

More information

Multi-class Classification: A Coding Based Space Partitioning

Multi-class Classification: A Coding Based Space Partitioning Multi-class Classification: A Coding Based Space Partitioning Sohrab Ferdowsi, Svyatoslav Voloshynovskiy, Marcin Gabryel, and Marcin Korytkowski University of Geneva, Centre Universitaire d Informatique,

More information

The Data Mining Process

The Data Mining Process Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data

More information

Support Vector Machine. Tutorial. (and Statistical Learning Theory)

Support Vector Machine. Tutorial. (and Statistical Learning Theory) Support Vector Machine (and Statistical Learning Theory) Tutorial Jason Weston NEC Labs America 4 Independence Way, Princeton, USA. jasonw@nec-labs.com 1 Support Vector Machines: history SVMs introduced

More information

Classification Problems

Classification Problems Classification Read Chapter 4 in the text by Bishop, except omit Sections 4.1.6, 4.1.7, 4.2.4, 4.3.3, 4.3.5, 4.3.6, 4.4, and 4.5. Also, review sections 1.5.1, 1.5.2, 1.5.3, and 1.5.4. Classification Problems

More information

Text Classification based on Lucene and LibSVM / LibLinear. Berlin Buzzwords, June 4th, 2012, Dr. Christoph Goller, IntraFind Software AG

Text Classification based on Lucene and LibSVM / LibLinear. Berlin Buzzwords, June 4th, 2012, Dr. Christoph Goller, IntraFind Software AG Text Classification based on Lucene and LibSVM / LibLinear Berlin Buzzwords, June 4th, 2012, Dr. Christoph Goller, IntraFind Software AG Outline IntraFind Software AG Introduction to Text Classification

More information

LVQ Plug-In Algorithm for SQL Server

LVQ Plug-In Algorithm for SQL Server LVQ Plug-In Algorithm for SQL Server Licínia Pedro Monteiro Instituto Superior Técnico licinia.monteiro@tagus.ist.utl.pt I. Executive Summary In this Resume we describe a new functionality implemented

More information

MHI3000 Big Data Analytics for Health Care Final Project Report

MHI3000 Big Data Analytics for Health Care Final Project Report MHI3000 Big Data Analytics for Health Care Final Project Report Zhongtian Fred Qiu (1002274530) http://gallery.azureml.net/details/81ddb2ab137046d4925584b5095ec7aa 1. Data pre-processing The data given

More information

Introduction to Bayesian Classification (A Practical Discussion) Todd Holloway Lecture for B551 Nov. 27, 2007

Introduction to Bayesian Classification (A Practical Discussion) Todd Holloway Lecture for B551 Nov. 27, 2007 Introduction to Bayesian Classification (A Practical Discussion) Todd Holloway Lecture for B551 Nov. 27, 2007 Naïve Bayes Components ML vs. MAP Benefits Feature Preparation Filtering Decay Extended Examples

More information

Chapter 12 Discovering New Knowledge Data Mining

Chapter 12 Discovering New Knowledge Data Mining Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to

More information

Data Mining for Web Personalization

Data Mining for Web Personalization 3 Data Mining for Web Personalization Bamshad Mobasher Center for Web Intelligence School of Computer Science, Telecommunication, and Information Systems DePaul University, Chicago, Illinois, USA mobasher@cs.depaul.edu

More information

Lasso-based Spam Filtering with Chinese Emails

Lasso-based Spam Filtering with Chinese Emails Journal of Computational Information Systems 8: 8 (2012) 3315 3322 Available at http://www.jofcis.com Lasso-based Spam Filtering with Chinese Emails Zunxiong LIU 1, Xianlong ZHANG 1,, Shujuan ZHENG 2 1

More information

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Knowledge Discovery and Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Evaluating the Accuracy of a Classifier Holdout, random subsampling, crossvalidation, and the bootstrap are common techniques for

More information

Detecting Corporate Fraud: An Application of Machine Learning

Detecting Corporate Fraud: An Application of Machine Learning Detecting Corporate Fraud: An Application of Machine Learning Ophir Gottlieb, Curt Salisbury, Howard Shek, Vishal Vaidyanathan December 15, 2006 ABSTRACT This paper explores the application of several

More information

Principles of Data Mining by Hand&Mannila&Smyth

Principles of Data Mining by Hand&Mannila&Smyth Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences

More information

Predicting Student Performance by Using Data Mining Methods for Classification

Predicting Student Performance by Using Data Mining Methods for Classification BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 13, No 1 Sofia 2013 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.2478/cait-2013-0006 Predicting Student Performance

More information

Predicting Soccer Match Results in the English Premier League

Predicting Soccer Match Results in the English Premier League Predicting Soccer Match Results in the English Premier League Ben Ulmer School of Computer Science Stanford University Email: ulmerb@stanford.edu Matthew Fernandez School of Computer Science Stanford University

More information

A Systemic Artificial Intelligence (AI) Approach to Difficult Text Analytics Tasks

A Systemic Artificial Intelligence (AI) Approach to Difficult Text Analytics Tasks A Systemic Artificial Intelligence (AI) Approach to Difficult Text Analytics Tasks Text Analytics World, Boston, 2013 Lars Hard, CTO Agenda Difficult text analytics tasks Feature extraction Bio-inspired

More information

Introduction to Data Mining

Introduction to Data Mining Introduction to Data Mining 1 Why Data Mining? Explosive Growth of Data Data collection and data availability Automated data collection tools, Internet, smartphones, Major sources of abundant data Business:

More information

SURVEY OF TEXT CLASSIFICATION ALGORITHMS FOR SPAM FILTERING

SURVEY OF TEXT CLASSIFICATION ALGORITHMS FOR SPAM FILTERING I J I T E ISSN: 2229-7367 3(1-2), 2012, pp. 233-237 SURVEY OF TEXT CLASSIFICATION ALGORITHMS FOR SPAM FILTERING K. SARULADHA 1 AND L. SASIREKA 2 1 Assistant Professor, Department of Computer Science and

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014 RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer

More information