What Is Data Mining? CMPT 354: Database I -- Data Mining 2
|
|
- Allison Sullivan
- 5 years ago
- Views:
Transcription
1 Data Mining
2 What Is Data Mining? Mining data mining knowledge Data mining is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data CMPT 354: Database I -- Data Mining 2
3 The KDD Process Knowledge Selection Target data Preprocessed data Transformed data Transformation Preprocessing Patterns Data mining Interpretation/ evaluation Data CMPT 354: Database I -- Data Mining 3
4 What Kind of Patterns? Association rules and sequential patterns Classification Clusters Many others CMPT 354: Database I -- Data Mining 4
5 Frequent Patterns and Association Rules (Time {Fri, Sat}) buy(x, diaper) buy(x, beer) Dads taking care of babies in weekends drink beers Itemsets should be frequent It can be applied extensively Rules should be strong, i.e., confident With strong prediction capability CMPT 354: Database I -- Data Mining 5
6 Sequential Patterns Frequent patterns in sequence databases Within 3 months, buy computer buy CD-ROM buy digital camera The (temporal) order is important CMPT 354: Database I -- Data Mining 6
7 Utilizations Find regularities in data What products were often purchased together? What are the subsequent purchases after buying a PC? Can we automatically classify web documents? What kinds of patients are sensitive to this new drug? Basket data analysis, cross-marketing, catalog design, sale campaign analysis, web log (click stream) analysis, CMPT 354: Database I -- Data Mining 7
8 Classification A decision tree for PlayTennis Day O utlook Temp Humid Wind P laytennis D 1 S unny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No Outlook Sunny Overcast Rain Humidity Yes Wind High Normal Strong Weak No Yes No Yes CMPT 354: Database I -- Data Mining 8
9 Utilizations Understanding the key features of large data sets Predictions Credit card approval Fraud detection Intrusion detection CMPT 354: Database I -- Data Mining 9
10 Clusters and Outliers Outliers Cluster 1 Cluster 2 Maximizing the intra-class similarity and minimizing the inter-class similarity CMPT 354: Database I -- Data Mining 10
11 Utilizations Data summarization Market/customer segmentation Pattern recognition Data preprocessing and compression Exception detection Fraud detection CMPT 354: Database I -- Data Mining 11
12 Frequent Patterns: Basics Itemset: a set of items E.g., acm={a, c, m} Support of itemsets Sup(acm)=3 Given min_sup = 3, acm is a frequent pattern Frequent pattern mining: find all frequent patterns in a database Sup(c am)=3 Conf(c am)=75% Transaction database TDB TID Items bought 100 f, a, c, d, g, I, m, p 200 a, b, c, f, l, m, o 300 b, f, h, j, o 400 b, c, k, s, p 500 a, f, c, e, l, p, m, n CMPT 354: Database I -- Data Mining 12
13 A Naïve Attempt Generate all possible itemsets, test their supports against the database 100 items possible itemets How to test the supports of a huge number of itemsets against a large database, say containing 1 million transactions? CMPT 354: Database I -- Data Mining 13
14 How to Get an Efficient Method? Reduce the number of itemsets that need to be checked Check the supports of selected itemsets efficiently CMPT 354: Database I -- Data Mining 14
15 Apriori: Anti-monotonic Property Any subset of a frequent itemset must be also frequent an anti-monotone property A transaction containing {beer, diaper, nuts} also contains {beer, diaper} {beer, diaper, nuts} is frequent {beer, diaper} must also be frequent In other words, any superset of an infrequent itemset must also be infrequent No superset of any infrequent itemset should be generated or tested Many item combinations can be pruned! CMPT 354: Database I -- Data Mining 15
16 Apriori-based Mining Candidate-generation-and-test Generate length (k+1) candidate itemsets from length k frequent itemsets, and Test the candidates against DB CMPT 354: Database I -- Data Mining 16
17 Apriori Algorithm A level-wise, candidate-generation-and-test approach (Agrawal & Srikant 1994) Data base D TID Items 10 a, c, d 20 b, c, e 30 a, b, c, e 40 b, e Min_sup=2 Scan D Scan D 3-candidates Itemset bce Freq 3-itemsets Itemset Sup bce 2 1-candidates Itemset Sup a 2 b 3 c 3 d 1 e 3 Freq 2-itemsets Itemset ac bc be ce Sup Freq 1-itemsets Itemset Sup a 2 b 3 c 3 e 3 Counting Itemset ab ac ae bc be ce Sup candidates Itemset ab ac ae bc be ce Scan D CMPT 354: Database I -- Data Mining 17
18 The Apriori Algorithm C k : Candidate itemset of size k L k : frequent itemset of size k L 1 = {frequent items}; for (k = 1; L k!= ; k++) do C k+1 = candidates generated from L k ; for each transaction t in database do increment the count of all candidates in C k+1 that are contained in t L k+1 = candidates in C k+1 with min_support return k L k ; CMPT 354: Database I -- Data Mining 18
19 Classification and Prediction Classification: predict categorical class labels Build a model for a set of classes/concepts Classify bank loan applications (safe/risky) Prediction: model continuous-valued functions Predict the economic growth in 2004 CMPT 354: Database I -- Data Mining 19
20 A Two-step Process Model construction: describe a set of predetermined classes Training dataset: tuples for model construction Each tuple/sample belongs to a predefined class Classification rules, decision trees, or math formulae Model application: classify unseen objects Estimate accuracy of the model using an independent test set Acceptable accuracy apply the model to classify tuples with unknown class labels CMPT 354: Database I -- Data Mining 20
21 Model Construction Training Data Classification Algorithms Name Rank Years Tenured Mike Ass. Prof 3 No Mary Ass. Prof 7 Yes Bill Prof 2 Yes Jim Asso. Prof 7 Yes Dave Ass. Prof 6 No Anne Asso. Prof 3 No Classifier (Model) IF rank = professor OR years > 6 THEN tenured = yes CMPT 354: Database I -- Data Mining 21
22 Model Application Classifier Testing Data Name Rank Years Tenured Tom Ass. Prof 2 No Merlisa Asso. Prof 7 No George Prof 5 Yes Joseph Ass. Prof 7 Yes Unseen Data (Jeff, Professor, 4) Tenured? CMPT 354: Database I -- Data Mining 22
23 Decision Tree A node in the tree a test of some attribute A branch: a possible value of the attribute Classification Start at the root Test the attribute Move down the tree branch Outlook Sunny Overcast Rain Humidity Yes Wind High Normal Strong Weak No Yes No Yes CMPT 354: Database I -- Data Mining 23
24 Training Dataset Outlook Temp Humid Wind PlayTennis Sunny Hot High Weak No Sunny Hot High Strong No Overcast Hot High Weak Yes Rain Mild High Weak Yes Rain Cool Normal Weak Yes Rain Cool Normal Strong No Overcast Cool Normal Strong Yes Sunny Mild High Weak No Sunny Cool Normal Weak Yes Rain Mild Normal Weak Yes Sunny Mild Normal Strong Yes Overcast Mild High Strong Yes Overcast Hot Normal Weak Yes Rain Mild High Strong No CMPT 354: Database I -- Data Mining 24
25 Basic Algorithm ID3 Construct a tree in a top-down recursive divideand-conquer manner Which attribute is the best at the current node? Create a nodes for each possible attribute value Partition training data into descendant nodes Conditions for stopping recursion All samples at a given node belong to the same class No attribute remained for further partitioning Majority voting is employed for classifying the leaf There is no sample at the node CMPT 354: Database I -- Data Mining 25
26 Which Attribute Is the Best? The attribute most useful for classifying examples Information gain and gini index Statistical properties Measure how well an attribute separates the training examples CMPT 354: Database I -- Data Mining 26
27 Entropy Measure homogeneity of examples Entropy( S) i= 1 S is the training data set, and pi is the proportion of S belong to class i The smaller the entropy, the purer the data set c p i log 2 p i CMPT 354: Database I -- Data Mining 27
28 Information Gain The expected reduction in entropy caused by partitioning the examples according to an attribute Gain( S, A) Entropy( S) v Values( A) S S v Entropy( S Value(A) is the set of all possible values for attribute A, and S v is the subset of S for which attribute A has value v v ) CMPT 354: Database I -- Data Mining 28
29 Example Entropy( S) = log 2 log = 0.94 Gain ( S, Wind = = Entropy ( S ) ) = Entropy ( S ) Engropy ( S v { Weak, Strong } Weak = ) Outlook Temp Humid Wind PlayTennis Sunny Hot High Weak No Sunny Hot High Strong No Overcast Hot High Weak Yes Rain Mild High Weak Yes Rain Cool Normal Weak Yes Rain Cool Normal Strong No Overcast Cool Normal Strong Yes Sunny Mild High Weak No Sunny Cool Normal Weak Yes Rain Mild Normal Weak Yes Sunny Mild Normal Strong Yes Overcast Mild High Strong Yes Overcast Hot Normal Weak Yes Rain Mild High Strong No CMPT 354: Database I -- Data Mining S S v Engropy ( S Entropy ( S Strong ) v )
30 Extracting Classification Rules Each path from the root to a leaf an IF- THEN rule Each attribute-value pair along a path forms a conjunction The leaf node holds the class prediction IF age = <=30 AND student = no THEN buys_computer = no Rules are easy to understand CMPT 354: Database I -- Data Mining 30
31 Summary Mining patterns Frequent patterns Classification Clustering Frequent patterns and Apriori algorithm Decision tree and ID3 algorithm CMPT 354: Database I -- Data Mining 31
Frequent Pattern Mining
Frequent Pattern Mining How Many Words Is a Picture Worth? E. Aiden and J-B Michel: Uncharted. Reverhead Books, 2013 Jian Pei: CMPT 741/459 Frequent Pattern Mining (1) 2 Burnt or Burned? E. Aiden and J-B
More informationBCB 713 Module Spring 2011
Association Rule Mining COMP 790-90 Seminar BCB 713 Module Spring 2011 The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Outline What is association rule mining? Methods for association rule mining Extensions
More informationThanks to the advances of data processing technologies, a lot of data can be collected and stored in databases efficiently New challenges: with a
Data Mining and Information Retrieval Introduction to Data Mining Why Data Mining? Thanks to the advances of data processing technologies, a lot of data can be collected and stored in databases efficiently
More informationCMPUT 391 Database Management Systems. Data Mining. Textbook: Chapter (without 17.10)
CMPUT 391 Database Management Systems Data Mining Textbook: Chapter 17.7-17.11 (without 17.10) University of Alberta 1 Overview Motivation KDD and Data Mining Association Rules Clustering Classification
More informationData warehouse and Data Mining
Data warehouse and Data Mining Lecture No. 14 Data Mining and its techniques Naeem A. Mahoto Email: naeemmahoto@gmail.com Department of Software Engineering Mehran Univeristy of Engineering and Technology
More informationChapter 4: Mining Frequent Patterns, Associations and Correlations
Chapter 4: Mining Frequent Patterns, Associations and Correlations 4.1 Basic Concepts 4.2 Frequent Itemset Mining Methods 4.3 Which Patterns Are Interesting? Pattern Evaluation Methods 4.4 Summary Frequent
More informationApriori Algorithm. 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke
Apriori Algorithm For a given set of transactions, the main aim of Association Rule Mining is to find rules that will predict the occurrence of an item based on the occurrences of the other items in the
More informationCOMP 465: Data Mining Classification Basics
Supervised vs. Unsupervised Learning COMP 465: Data Mining Classification Basics Slides Adapted From : Jiawei Han, Micheline Kamber & Jian Pei Data Mining: Concepts and Techniques, 3 rd ed. Supervised
More informationCS570 Introduction to Data Mining
CS570 Introduction to Data Mining Frequent Pattern Mining and Association Analysis Cengiz Gunay Partial slide credits: Li Xiong, Jiawei Han and Micheline Kamber George Kollios 1 Mining Frequent Patterns,
More informationExtra readings beyond the lecture slides are important:
1 Notes To preview next lecture: Check the lecture notes, if slides are not available: http://web.cse.ohio-state.edu/~sun.397/courses/au2017/cse5243-new.html Check UIUC course on the same topic. All their
More informationChapter 6: Basic Concepts: Association Rules. Basic Concepts: Frequent Patterns. (absolute) support, or, support. (relative) support, s, is the
Chapter 6: What Is Frequent ent Pattern Analysis? Frequent pattern: a pattern (a set of items, subsequences, substructures, etc) that occurs frequently in a data set frequent itemsets and association rule
More informationData Mining: Concepts and Techniques Classification and Prediction Chapter 6.1-3
Data Mining: Concepts and Techniques Classification and Prediction Chapter 6.1-3 January 25, 2007 CSE-4412: Data Mining 1 Chapter 6 Classification and Prediction 1. What is classification? What is prediction?
More informationBasic Data Mining Technique
Basic Data Mining Technique What is classification? What is prediction? Supervised and Unsupervised Learning Decision trees Association rule K-nearest neighbor classifier Case-based reasoning Genetic algorithm
More informationCSE 634/590 Data mining Extra Credit: Classification by Association rules: Example Problem. Muhammad Asiful Islam, SBID:
CSE 634/590 Data mining Extra Credit: Classification by Association rules: Example Problem Muhammad Asiful Islam, SBID: 106506983 Original Data Outlook Humidity Wind PlayTenis Sunny High Weak No Sunny
More informationLecture Topic Projects 1 Intro, schedule, and logistics 2 Data Science components and tasks 3 Data types Project #1 out 4 Introduction to R,
Lecture Topic Projects 1 Intro, schedule, and logistics 2 Data Science components and tasks 3 Data types Project #1 out 4 Introduction to R, statistics foundations 5 Introduction to D3, visual analytics
More informationClassification with Decision Tree Induction
Classification with Decision Tree Induction This algorithm makes Classification Decision for a test sample with the help of tree like structure (Similar to Binary Tree OR k-ary tree) Nodes in the tree
More informationNesnelerin İnternetinde Veri Analizi
Bölüm 4. Frequent Patterns in Data Streams w3.gazi.edu.tr/~suatozdemir What Is Pattern Discovery? What are patterns? Patterns: A set of items, subsequences, or substructures that occur frequently together
More informationAssociation Rule Mining. Entscheidungsunterstützungssysteme
Association Rule Mining Entscheidungsunterstützungssysteme Frequent Pattern Analysis Frequent pattern: a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set
More informationFrequent Pattern Mining
Frequent Pattern Mining...3 Frequent Pattern Mining Frequent Patterns The Apriori Algorithm The FP-growth Algorithm Sequential Pattern Mining Summary 44 / 193 Netflix Prize Frequent Pattern Mining Frequent
More informationData Mining Part 3. Associations Rules
Data Mining Part 3. Associations Rules 3.2 Efficient Frequent Itemset Mining Methods Fall 2009 Instructor: Dr. Masoud Yaghini Outline Apriori Algorithm Generating Association Rules from Frequent Itemsets
More informationAssociation Rules. A. Bellaachia Page: 1
Association Rules 1. Objectives... 2 2. Definitions... 2 3. Type of Association Rules... 7 4. Frequent Itemset generation... 9 5. Apriori Algorithm: Mining Single-Dimension Boolean AR 13 5.1. Join Step:...
More informationAssociation rules. Marco Saerens (UCL), with Christine Decaestecker (ULB)
Association rules Marco Saerens (UCL), with Christine Decaestecker (ULB) 1 Slides references Many slides and figures have been adapted from the slides associated to the following books: Alpaydin (2004),
More informationChapter 4 Data Mining A Short Introduction. 2005/6, Karl Aberer, EPFL-IC, Laboratoire de systèmes d'informations répartis Data Mining - 1
Chapter 4 Data Mining A Short Introduction 2005/6, Karl Aberer, EPFL-IC, Laboratoire de systèmes d'informations répartis Data Mining - 1 1 Today's Question 1. Data Mining Overview 2. Association Rule Mining
More informationData Mining: Concepts and Techniques. Chapter 5. SS Chung. April 5, 2013 Data Mining: Concepts and Techniques 1
Data Mining: Concepts and Techniques Chapter 5 SS Chung April 5, 2013 Data Mining: Concepts and Techniques 1 Chapter 5: Mining Frequent Patterns, Association and Correlations Basic concepts and a road
More informationPattern Mining. Knowledge Discovery and Data Mining 1. Roman Kern KTI, TU Graz. Roman Kern (KTI, TU Graz) Pattern Mining / 42
Pattern Mining Knowledge Discovery and Data Mining 1 Roman Kern KTI, TU Graz 2016-01-14 Roman Kern (KTI, TU Graz) Pattern Mining 2016-01-14 1 / 42 Outline 1 Introduction 2 Apriori Algorithm 3 FP-Growth
More informationAssociation Rules Apriori Algorithm
Association Rules Apriori Algorithm Market basket analysis n Market basket analysis might tell a retailer that customers often purchase shampoo and conditioner n Putting both items on promotion at the
More informationChapter 7: Frequent Itemsets and Association Rules
Chapter 7: Frequent Itemsets and Association Rules Information Retrieval & Data Mining Universität des Saarlandes, Saarbrücken Winter Semester 2013/14 VII.1&2 1 Motivational Example Assume you run an on-line
More informationANU MLSS 2010: Data Mining. Part 2: Association rule mining
ANU MLSS 2010: Data Mining Part 2: Association rule mining Lecture outline What is association mining? Market basket analysis and association rule examples Basic concepts and formalism Basic rule measurements
More informationFundamental Data Mining Algorithms
2018 EE448, Big Data Mining, Lecture 3 Fundamental Data Mining Algorithms Weinan Zhang Shanghai Jiao Tong University http://wnzhang.net http://wnzhang.net/teaching/ee448/index.html REVIEW What is Data
More informationMining Association Rules in Large Databases
Mining Association Rules in Large Databases Association rules Given a set of transactions D, find rules that will predict the occurrence of an item (or a set of items) based on the occurrences of other
More informationFrequent Pattern Mining. Based on: Introduction to Data Mining by Tan, Steinbach, Kumar
Frequent Pattern Mining Based on: Introduction to Data Mining by Tan, Steinbach, Kumar Item sets A New Type of Data Some notation: All possible items: Database: T is a bag of transactions Transaction transaction
More informationDecision Tree CE-717 : Machine Learning Sharif University of Technology
Decision Tree CE-717 : Machine Learning Sharif University of Technology M. Soleymani Fall 2012 Some slides have been adapted from: Prof. Tom Mitchell Decision tree Approximating functions of usually discrete
More informationProduct presentations can be more intelligently planned
Association Rules Lecture /DMBI/IKI8303T/MTI/UI Yudho Giri Sucahyo, Ph.D, CISA (yudho@cs.ui.ac.id) Faculty of Computer Science, Objectives Introduction What is Association Mining? Mining Association Rules
More informationPrediction. What is Prediction. Simple methods for Prediction. Classification by decision tree induction. Classification and regression evaluation
Prediction Prediction What is Prediction Simple methods for Prediction Classification by decision tree induction Classification and regression evaluation 2 Prediction Goal: to predict the value of a given
More informationClassification and Prediction
Objectives Introduction What is Classification? Classification vs Prediction Supervised and Unsupervised Learning D t P Data Preparation ti Classification Accuracy ID3 Algorithm Information Gain Bayesian
More informationAssociation Rules Apriori Algorithm
Association Rules Apriori Algorithm Market basket analysis n Market basket analysis might tell a retailer that customers often purchase shampoo and conditioner n Putting both items on promotion at the
More informationMining Frequent Patterns without Candidate Generation
Mining Frequent Patterns without Candidate Generation Outline of the Presentation Outline Frequent Pattern Mining: Problem statement and an example Review of Apriori like Approaches FP Growth: Overview
More informationEffectiveness of Freq Pat Mining
Effectiveness of Freq Pat Mining Too many patterns! A pattern a 1 a 2 a n contains 2 n -1 subpatterns Understanding many patterns is difficult or even impossible for human users Non-focused mining A manager
More informationBasic Concepts: Association Rules. What Is Frequent Pattern Analysis? COMP 465: Data Mining Mining Frequent Patterns, Associations and Correlations
What Is Frequent Pattern Analysis? COMP 465: Data Mining Mining Frequent Patterns, Associations and Correlations Slides Adapted From : Jiawei Han, Micheline Kamber & Jian Pei Data Mining: Concepts and
More informationKnowledge Discovery in Databases
Ludwig-Maximilians-Universität München Institut für Informatik Lehr- und Forschungseinheit für Datenbanksysteme Lecture notes Knowledge Discovery in Databases Summer Semester 2012 Lecture 4: Classification
More informationData Mining: Concepts and Techniques. (3 rd ed.) Chapter 6
Data Mining: Concepts and Techniques (3 rd ed.) Chapter 6 Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University 2013-2017 Han, Kamber & Pei. All
More informationAssociation Rule Mining
Association Rule Mining Generating assoc. rules from frequent itemsets Assume that we have discovered the frequent itemsets and their support How do we generate association rules? Frequent itemsets: {1}
More informationDecision Support Systems
Decision Support Systems 2011/2012 Week 6. Lecture 11 HELLO DATA MINING! THE PLAN: MINING FREQUENT PATTERNS (Classes 11-13) Homework 5 CLUSTER ANALYSIS (Classes 14-16) Homework 6 SUPERVISED LEARNING (Classes
More informationChapter 4: Association analysis:
Chapter 4: Association analysis: 4.1 Introduction: Many business enterprises accumulate large quantities of data from their day-to-day operations, huge amounts of customer purchase data are collected daily
More informationAssociation Rules. Berlin Chen References:
Association Rules Berlin Chen 2005 References: 1. Data Mining: Concepts, Models, Methods and Algorithms, Chapter 8 2. Data Mining: Concepts and Techniques, Chapter 6 Association Rules: Basic Concepts A
More informationData Mining for Knowledge Management. Association Rules
1 Data Mining for Knowledge Management Association Rules Themis Palpanas University of Trento http://disi.unitn.eu/~themis 1 Thanks for slides to: Jiawei Han George Kollios Zhenyu Lu Osmar R. Zaïane Mohammad
More informationData Mining Course Overview
Data Mining Course Overview 1 Data Mining Overview Understanding Data Classification: Decision Trees and Bayesian classifiers, ANN, SVM Association Rules Mining: APriori, FP-growth Clustering: Hierarchical
More informationBITS F464: MACHINE LEARNING
BITS F464: MACHINE LEARNING Lecture-16: Decision Tree (contd.) + Random Forest Dr. Kamlesh Tiwari Assistant Professor Department of Computer Science and Information Systems Engineering, BITS Pilani, Rajasthan-333031
More informationMarket baskets Frequent itemsets FP growth. Data mining. Frequent itemset Association&decision rule mining. University of Szeged.
Frequent itemset Association&decision rule mining University of Szeged What frequent itemsets could be used for? Features/observations frequently co-occurring in some database can gain us useful insights
More informationData Mining Concepts
Data Mining Concepts Outline Data Mining Data Warehousing Knowledge Discovery in Databases (KDD) Goals of Data Mining and Knowledge Discovery Association Rules Additional Data Mining Algorithms Sequential
More informationData Structures. Notes for Lecture 14 Techniques of Data Mining By Samaher Hussein Ali Association Rules: Basic Concepts and Application
Data Structures Notes for Lecture 14 Techniques of Data Mining By Samaher Hussein Ali 2009-2010 Association Rules: Basic Concepts and Application 1. Association rules: Given a set of transactions, find
More informationIntroduction to Data Mining. Yücel SAYGIN
Introduction to Data Mining Yücel SAYGIN ysaygin@sabanciuniv.edu http://people.sabanciuniv.edu/~ysaygin/ A Brief History Historically, we had operational databases, ex: for accounts, customers, personnel
More informationFrequent Pattern Mining S L I D E S B Y : S H R E E J A S W A L
Frequent Pattern Mining S L I D E S B Y : S H R E E J A S W A L Topics to be covered Market Basket Analysis, Frequent Itemsets, Closed Itemsets, and Association Rules; Frequent Pattern Mining, Efficient
More informationAssociation Rules. Charles Sutton Data Mining and Exploration Spring Based on slides by Chris Williams and Amos Storkey. Thursday, 8 March 12
Association Rules Charles Sutton Data Mining and Exploration Spring 2012 Based on slides by Chris Williams and Amos Storkey The Goal Find patterns : local regularities that occur more often than you would
More informationIntroduction to Machine Learning
Introduction to Machine Learning Decision Tree Example Three variables: Attribute 1: Hair = {blond, dark} Attribute 2: Height = {tall, short} Class: Country = {Gromland, Polvia} CS4375 --- Fall 2018 a
More informationCSE 5243 INTRO. TO DATA MINING
CSE 5243 INTRO. TO DATA MINING Mining Frequent Patterns and Associations: Basic Concepts (Chapter 6) Huan Sun, CSE@The Ohio State University 10/19/2017 Slides adapted from Prof. Jiawei Han @UIUC, Prof.
More informationData Warehousing & Mining. Data integration. OLTP versus OLAP. CPS 116 Introduction to Database Systems
Data Warehousing & Mining CPS 116 Introduction to Database Systems Data integration 2 Data resides in many distributed, heterogeneous OLTP (On-Line Transaction Processing) sources Sales, inventory, customer,
More informationData Warehousing and Data Mining. Announcements (December 1) Data integration. CPS 116 Introduction to Database Systems
Data Warehousing and Data Mining CPS 116 Introduction to Database Systems Announcements (December 1) 2 Homework #4 due today Sample solution available Thursday Course project demo period has begun! Check
More informationRule induction. Dr Beatriz de la Iglesia
Rule induction Dr Beatriz de la Iglesia email: b.iglesia@uea.ac.uk Outline What are rules? Rule Evaluation Classification rules Association rules 2 Rule induction (RI) As their name suggests, RI algorithms
More information2 CONTENTS
Contents 5 Mining Frequent Patterns, Associations, and Correlations 3 5.1 Basic Concepts and a Road Map..................................... 3 5.1.1 Market Basket Analysis: A Motivating Example........................
More informationMachine Learning: Symbolische Ansätze
Machine Learning: Symbolische Ansätze Unsupervised Learning Clustering Association Rules V2.0 WS 10/11 J. Fürnkranz Different Learning Scenarios Supervised Learning A teacher provides the value for the
More informationChapter 4 Data Mining A Short Introduction
Chapter 4 Data Mining A Short Introduction Data Mining - 1 1 Today's Question 1. Data Mining Overview 2. Association Rule Mining 3. Clustering 4. Classification Data Mining - 2 2 1. Data Mining Overview
More informationData Mining. 3.2 Decision Tree Classifier. Fall Instructor: Dr. Masoud Yaghini. Chapter 5: Decision Tree Classifier
Data Mining 3.2 Decision Tree Classifier Fall 2008 Instructor: Dr. Masoud Yaghini Outline Introduction Basic Algorithm for Decision Tree Induction Attribute Selection Measures Information Gain Gain Ratio
More informationChapter 13, Sequence Data Mining
CSI 4352, Introduction to Data Mining Chapter 13, Sequence Data Mining Young-Rae Cho Associate Professor Department of Computer Science Baylor University Topics Single Sequence Mining Frequent sequence
More informationRoadmap DB Sys. Design & Impl. Association rules - outline. Citations. Association rules - idea. Association rules - idea.
15-721 DB Sys. Design & Impl. Association Rules Christos Faloutsos www.cs.cmu.edu/~christos Roadmap 1) Roots: System R and Ingres... 7) Data Analysis - data mining datacubes and OLAP classifiers association
More informationImproved Frequent Pattern Mining Algorithm with Indexing
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 16, Issue 6, Ver. VII (Nov Dec. 2014), PP 73-78 Improved Frequent Pattern Mining Algorithm with Indexing Prof.
More informationChapter 6: Association Rules
Chapter 6: Association Rules Association rule mining Proposed by Agrawal et al in 1993. It is an important data mining model. Transaction data (no time-dependent) Assume all data are categorical. No good
More informationInterestingness Measurements
Interestingness Measurements Objective measures Two popular measurements: support and confidence Subjective measures [Silberschatz & Tuzhilin, KDD95] A rule (pattern) is interesting if it is unexpected
More informationAssociation Rule Discovery
Association Rule Discovery Association Rules describe frequent co-occurences in sets an item set is a subset A of all possible items I Example Problems: Which products are frequently bought together by
More informationInfrequent Weighted Itemset Mining Using SVM Classifier in Transaction Dataset
Infrequent Weighted Itemset Mining Using SVM Classifier in Transaction Dataset M.Hamsathvani 1, D.Rajeswari 2 M.E, R.Kalaiselvi 3 1 PG Scholar(M.E), Angel College of Engineering and Technology, Tiruppur,
More informationSupervised and Unsupervised Learning (II)
Supervised and Unsupervised Learning (II) Yong Zheng Center for Web Intelligence DePaul University, Chicago IPD 346 - Data Science for Business Program DePaul University, Chicago, USA Intro: Supervised
More informationData Mining Techniques
Data Mining Techniques CS 6220 - Section 3 - Fall 2016 Lecture 16: Association Rules Jan-Willem van de Meent (credit: Yijun Zhao, Yi Wang, Tan et al., Leskovec et al.) Apriori: Summary All items Count
More informationJarek Szlichta
Jarek Szlichta http://data.science.uoit.ca/ Approximate terminology, though there is some overlap: Data(base) operations Executing specific operations or queries over data Data mining Looking for patterns
More informationData Mining Classification - Part 1 -
Data Mining Classification - Part 1 - Universität Mannheim Bizer: Data Mining I FSS2019 (Version: 20.2.2018) Slide 1 Outline 1. What is Classification? 2. K-Nearest-Neighbors 3. Decision Trees 4. Model
More informationCredit card Fraud Detection using Predictive Modeling: a Review
February 207 IJIRT Volume 3 Issue 9 ISSN: 2396002 Credit card Fraud Detection using Predictive Modeling: a Review Varre.Perantalu, K. BhargavKiran 2 PG Scholar, CSE, Vishnu Institute of Technology, Bhimavaram,
More informationData Mining. Decision Tree. Hamid Beigy. Sharif University of Technology. Fall 1396
Data Mining Decision Tree Hamid Beigy Sharif University of Technology Fall 1396 Hamid Beigy (Sharif University of Technology) Data Mining Fall 1396 1 / 24 Table of contents 1 Introduction 2 Decision tree
More informationChapter 28. Outline. Definitions of Data Mining. Data Mining Concepts
Chapter 28 Data Mining Concepts Outline Data Mining Data Warehousing Knowledge Discovery in Databases (KDD) Goals of Data Mining and Knowledge Discovery Association Rules Additional Data Mining Algorithms
More informationAssociation rule mining
Association rule mining Association rule induction: Originally designed for market basket analysis. Aims at finding patterns in the shopping behavior of customers of supermarkets, mail-order companies,
More informationCarnegie Mellon Univ. Dept. of Computer Science /615 DB Applications. Data mining - detailed outline. Problem
Faloutsos & Pavlo 15415/615 Carnegie Mellon Univ. Dept. of Computer Science 15415/615 DB Applications Lecture # 24: Data Warehousing / Data Mining (R&G, ch 25 and 26) Data mining detailed outline Problem
More informationPATTERN DISCOVERY IN TIME-ORIENTED DATA
PATTERN DISCOVERY IN TIME-ORIENTED DATA Mohammad Saraee, George Koundourakis and Babis Theodoulidis TimeLab Information Management Group Department of Computation, UMIST, Manchester, UK Email: saraee,
More informationDATA MINING II - 1DL460
DATA MINING II - 1DL460 Spring 2013 " An second class in data mining http://www.it.uu.se/edu/course/homepage/infoutv2/vt13 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology,
More informationChapter 1, Introduction
CSI 4352, Introduction to Data Mining Chapter 1, Introduction Young-Rae Cho Associate Professor Department of Computer Science Baylor University What is Data Mining? Definition Knowledge Discovery from
More informationCse634 DATA MINING TEST REVIEW. Professor Anita Wasilewska Computer Science Department Stony Brook University
Cse634 DATA MINING TEST REVIEW Professor Anita Wasilewska Computer Science Department Stony Brook University Preprocessing stage Preprocessing: includes all the operations that have to be performed before
More informationAn Improved Apriori Algorithm for Association Rules
Research article An Improved Apriori Algorithm for Association Rules Hassan M. Najadat 1, Mohammed Al-Maolegi 2, Bassam Arkok 3 Computer Science, Jordan University of Science and Technology, Irbid, Jordan
More informationCOMP90049 Knowledge Technologies
COMP90049 Knowledge Technologies Data Mining (Lecture Set 3) 2017 Rao Kotagiri Department of Computing and Information Systems The Melbourne School of Engineering Some of slides are derived from Prof Vipin
More informationData mining - detailed outline. Carnegie Mellon Univ. Dept. of Computer Science /615 DB Applications. Problem.
Faloutsos & Pavlo 15415/615 Carnegie Mellon Univ. Dept. of Computer Science 15415/615 DB Applications Data Warehousing / Data Mining (R&G, ch 25 and 26) C. Faloutsos and A. Pavlo Data mining detailed outline
More informationNesnelerin İnternetinde Veri Analizi
Nesnelerin İnternetinde Veri Analizi Bölüm 3. Classification in Data Streams w3.gazi.edu.tr/~suatozdemir Supervised vs. Unsupervised Learning (1) Supervised learning (classification) Supervision: The training
More informationClassification by Association
Classification by Association Cse352 Ar*ficial Intelligence Professor Anita Wasilewska Generating Classification Rules by Association When mining associa&on rules for use in classifica&on we are only interested
More informationA Novel method for Frequent Pattern Mining
A Novel method for Frequent Pattern Mining K.Rajeswari #1, Dr.V.Vaithiyanathan *2 # Associate Professor, PCCOE & Ph.D Research Scholar SASTRA University, Tanjore, India 1 raji.pccoe@gmail.com * Associate
More informationKnowledge Discovery in Data Bases
Knowledge Discovery in Data Bases Chien-Chung Chan Department of CS University of Akron Akron, OH 44325-4003 2/24/99 1 Why KDD? We are drowning in information, but starving for knowledge John Naisbett
More informationChapter 7: Frequent Itemsets and Association Rules
Chapter 7: Frequent Itemsets and Association Rules Information Retrieval & Data Mining Universität des Saarlandes, Saarbrücken Winter Semester 2011/12 VII.1-1 Chapter VII: Frequent Itemsets and Association
More informationCSE 5243 INTRO. TO DATA MINING
CSE 5243 INTRO. TO DATA MINING Mining Frequent Patterns and Associations: Basic Concepts (Chapter 6) Huan Sun, CSE@The Ohio State University Slides adapted from Prof. Jiawei Han @UIUC, Prof. Srinivasan
More informationAssociation Rule Discovery
Association Rule Discovery Association Rules describe frequent co-occurences in sets an itemset is a subset A of all possible items I Example Problems: Which products are frequently bought together by
More informationTutorial on Association Rule Mining
Tutorial on Association Rule Mining Yang Yang yang.yang@itee.uq.edu.au DKE Group, 78-625 August 13, 2010 Outline 1 Quick Review 2 Apriori Algorithm 3 FP-Growth Algorithm 4 Mining Flickr and Tag Recommendation
More informationDecision tree learning
Decision tree learning Andrea Passerini passerini@disi.unitn.it Machine Learning Learning the concept Go to lesson OUTLOOK Rain Overcast Sunny TRANSPORTATION LESSON NO Uncovered Covered Theoretical Practical
More informationPFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures
PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures 1 Introduction Frequent itemset mining is a popular data mining task. It consists of discovering sets of items (itemsets) frequently
More informationISSUES IN DECISION TREE LEARNING
ISSUES IN DECISION TREE LEARNING Handling Continuous Attributes Other attribute selection measures Overfitting-Pruning Handling of missing values Incremental Induction of Decision Tree 1 DECISION TREE
More informationKnowledge Discovery and Data Mining
Knowledge Discovery and Data Mining Unit # 1 1 Acknowledgement Several Slides in this presentation are taken from course slides provided by Han and Kimber (Data Mining Concepts and Techniques) and Tan,
More informationHigh dim. data. Graph data. Infinite data. Machine learning. Apps. Locality sensitive hashing. Filtering data streams.
http://www.mmds.org High dim. data Graph data Infinite data Machine learning Apps Locality sensitive hashing PageRank, SimRank Filtering data streams SVM Recommen der systems Clustering Network Analysis
More informationComparing the Performance of Frequent Itemsets Mining Algorithms
Comparing the Performance of Frequent Itemsets Mining Algorithms Kalash Dave 1, Mayur Rathod 2, Parth Sheth 3, Avani Sakhapara 4 UG Student, Dept. of I.T., K.J.Somaiya College of Engineering, Mumbai, India
More information