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Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course on Data Mining (581550-4) Intro/Ass. Rules 7.11. 24./26.10. Clustering 14.11. Episodes KDD Process Home Exam 30.10. Text Mining 21.11. 28.11. Appl./Summary Course on Data Mining Page 1/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Accepted to Autumn 2001 Course Arkko Jouko Löfström Jaakko Sahlberg Mauri Asikainen Tomi Malinen Johanna Saikku Arja Aunimo Lili Mäkelä Eetu Sundman Jonas Hyvönen Leena Ojala Petri Tarvainen Tero Johansson Carl Palin Kimmo Tiihonen Sami Jokinen Sakari Pasanen Janne Tolvanen Juha Kerminen Antti Pietilä Mikko Uusitalo Petri Kuokkanen Ville Pitkänen Esa Vasankari Minna Lehmussaari Kari Rapiokallio Maarit Lehtonen Miro Roos Teemu Virtanen Otso Course on Data Mining Page 2/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Lecturers Lectures Course Material Exercises Contents Course on Data Mining Page 3/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Dr. Mika Klemettinen • PhD Mika Klemettinen: – – – – • Email: [email protected] WWW: http://www.cs.helsinki.fi/u/mklemett/ Room: B356 Tel: 050-483 6661 PhD in January 1999: – Thesis: A Knowledge Discovery Methodology for Telecommunication Network Alarm Databases • Data mining and SGML/XML related research at UH/CS (1994-2000) and at Nokia (2000-) Course on Data Mining Page 4/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Dr. Pirjo Moen • PhD Pirjo Moen: – – – – • Email: [email protected] WWW: http://www.cs.helsinki.fi/pirjo.moen/ Room: B350 Tel:191 44238 PhD in February 2000: – Thesis: Attribute, Event Sequence, and Event Type Similarity Notions for Data Mining • Data mining related research at UH/CS (1994-) Course on Data Mining Page 5/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization DM/SGML/XML at UH/CS • RATI (A structured text database system/ Rakenteiset tekstitietokannat), 1988-91 • Data mining from telecommunication alarm data, 1994-97 • Structured and Intelligent Documents (SID), 1995-98 • From Data to Knowledge (FDK), 1995- • Knowledge worker’s workstation (TYTTI), 2000-02 • DM Group (99), DOREMI Group (00) Linux was invented here! Course on Data Mining Page 6/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization NRC in Short • • • • Nokia is the global leader in digital communication technologies with around 60 000 employees all over the world Nokia Research Center (NRC) has around 1 200 employees in Finland, USA, Japan, China, Germany, Hungary, UK, etc. NRC's role is to enhance the Nokia's technological competitiveness by exploring and developing new technologies Strongly involved in many European Union and national research projects Course on Data Mining Page 7/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization DM Group at NRC • • Background: – At the University of Computer Science data mining methods and theory of data mining since late 80´s – Association and episode rule mining, time series similarity, analysis of telecommunication alarm data and web logs, etc. Other members include: – Dr. Heikki Mannila (group leader) – Dr. Hannu Toivonen Course on Data Mining Page 8/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Lectures (1) • 24.10.-30.11.2001 (12 lectures): – 7 normal lectures – 5 seminar like lectures • Wed 14-16, Fri 12-14 (A217): – Wed: normal lecture – Fri: seminar like lecture (except for 26.10.) • Lectures are obligatory: – Normal lectures: 5/7 – Seminar like lectures: 4/5 • Lists are circulated Course on Data Mining Page 9/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Lectures (2) • Lecturing language is Finnish, slides are in English: – Students can also use English – A foreign student group can be established • Normal lectures: – Basics, terminology, standard methods – Lecturer driven teaching • Seminar like lectures: – Extensions to the basic methods – Lecturer gives an introduction – Student groups give short presentations Course on Data Mining Page 10/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Lectures (3) • Group for seminar (and exercise) work: – 10 groups, à 3 persons, 2 groups/lecture – Dates are agreed at the beginning of course – Articles are given on previous week's Wed • Seminar presentations: – Presentation in an HTML page (around 3-5 printed pages) due to seminar starting: • Can be either a HTML page or a printable document in PostScript/PDF format – 30 minutes of presentation – 5-15 minutes of discussion – Active participation Course on Data Mining Page 11/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Course Material • • • • Lecture slides Original articles Seminar presentations Book: "Data Mining: Concepts and Techniques" by Jiawei Han and Micheline Kamber, Morgan Kaufmann Publishers, August 2000. 550 pages. ISBN 1-55860489-8 • Remember to check course website and folder for the material! Course on Data Mining Page 12/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Exercises • Given by Pirjo Moen: – Email: [email protected] – Room: B350 – Tel: 191 44238 • • • 1.11.-29.11.2001 (5 exercises) Thu 12-14 (A318) Exercises are obligatory: – Exercises: 4/5 • • Lists are circulated Discussion is an essential part! Course on Data Mining Page 13/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Exercises • Usually around 3-4 exercises: – 2-3 "normal" exercises (with subtasks): • Available due Thu mornings at 9 – 1 group work: • A practical exercise • Available due Thu mornings at 9 • A written report (not hand-written!) must be returned at the exercise session • Group = the seminar presentation group • Foreign students: – Return all exercises in written format to Pirjo Moen Course on Data Mining Page 14/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Home Exam • • • The home exam is given on 28.11.2001 Must be returned by 21.12.2001 (printed version, not hand-written, not by email) Tentatively: – Course lectures, seminar presentations and exercises are the material for the exam – Questions contain both theoretical and practical issues – Around 4-6 smaller questions – Around 1-2 bigger questions Course on Data Mining Page 15/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Course Evaluation • • Scale: 1-/3 … 3/3 or rejected Grade = home exam + exercises + experiments + group presentations: – home exam: max 30 points • (4 X 5p) + (1 X 10p) – normal exercises (10): max 5 points • 2: 1p, 4: 2p, 6: 3p, 8: 4p, 10: 5p – experiments (5): max 15 points • max 3 points/experiment – group presentation: max 10 points Course on Data Mining Page 16/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Course Evaluation • Passing the course: min 30 points – home exam: min 13 points (max 30 points) – exercises/experiments: min 8 points (max 20 points) • at least 3 returned and reported experiments – group presentation: min 4 points (max 10 points) • Remember also the other requirements: – Attending the lectures (5/7) – Attending the seminars (4/5) – Attending the exercises (4/5) Course on Data Mining Page 17/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Course Contents (1) • Module/Week 1: – – – – • What is Data Mining? Association rules 24.10. normal lecture by Mika 26.10. normal lecture by Mika Module/Week 2: – – – – Recurrent patterns Episode rules, minimal occurrences 31.10. normal lecture by Mika 2.11. seminar like lecture by Pirjo Course on Data Mining Page 18/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Course Contents (2) • Module/Week 3: – Text mining – 7.11. normal lecture by Mika – 9.11. seminar like lecture by Mika • Module/Week 4: – – – – – Clustering Classification Similarity 14.11. normal lecture by Pirjo 16.11. seminar like lecture by Mika Course on Data Mining Page 19/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization Course Contents (3) • Module/Week 5: – – – – • Knowledge discovery process Pre- and postprocessing 21.11. normal lecture by Pirjo 23.11. seminar like lecture by Pirjo Module/Week 6: – – – – Data mining tools Summary, future 28.11. normal lecture by Pirjo 30.11. seminar like lecture by Pirjo Course on Data Mining Page 20/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization / Groups Group Establishment • • Group is for both seminar and weekly group exercise work 10 groups à 3 persons Get grouped! Course on Data Mining Page 21/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization / Groups • Group presentation time allocation: – Fri 2.11.: Group 1, Group 2 (associations) – Fri 9.11.: Group 3, Group 4 (episodes) – Fri 16.11.: Group 5, Group 6 (text mining) – Fri 23.11.: Group 7, Group 8 (clustering) – Fri 30.11.: Group 9, Group 10 (KDD process) Course on Data Mining Page 22/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization / Groups • Group 1: – Asikainen Tomi, Hyvönen Leena • Group 2: – Löfström Jaakko, Pitkänen Esa, Tarvainen Tero • Group 3: – Jokinen Sakari, Kuokkanen Ville, Tolvanen Juha • Group 4: – Lehmussaari Kari, Pietilä Mikko, Uusitalo Petri • Group 5: – Johansson Carl, Kerminen Antti, Sundman Jonas Course on Data Mining Page 23/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Course Organization / Groups • Group 6: – Malinen Johanna, Sahlberg Mauri, Vasankari Minna • Group 7: – Arkko Jouko, Ojala Petri, Rapiokallio Maarit • Group 8: – Palin Kimmo, Pasanen Janne (, X) • Group 9: – Aunimo Lili, Lehtonen Miro, Saikku Arja • Group 10: – X, X, X Course on Data Mining Page 24/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Introduction to Data Mining (DM) What? Why? Applications KDD Process DM Views Major Issues Course on Data Mining Page 25/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Computers in 1940s (ENIAC) Course on Data Mining Page 26/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Personal Home Network in 2000s File Edit Locate View Storage H elp 500 E D C B A 400 300 200 100 0 1 2 3 4 5 6 Network Traffic 7 Mount 431 7437 1950 79% / 02 631963 47358 Help 93% /us Storage Storage Storage Storage Storage Storage Internet Storage Course on Data Mining Page 27/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Evolution of Database Technology • 1960s: – Data collection, database creation, IMS and network DBMS • 1970s: – Relational data model, relational DBMS implementation • 1980s: – RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.) • 1990s: – Data mining and data warehousing, multimedia databases, and Web technology Course on Data Mining Page 28/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Why Data Mining? • Enormous amounts of data available: – Automated data collection tools and mature database technology lead to huge amounts of data stored in databases, data warehouses and other information repositories – Manual inspection is either tedious or just impossible Course on Data Mining Page 29/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 What is Data Mining? • Ultimately: – "Extraction of interesting (non-trivial, implicit, previously unknown, potentially useful) information or patterns from data in large databases" • Often just: – "Tell something interesting about this data", "Describe this data" Exploratory, semi-automatic data analysis on large data sets Course on Data Mining Page 30/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 What is Data Mining? • Rather established terminology: – Data mining • Usually DM is one part of KDD process – Knowledge discovery in databases (KDD) • The general term that covers, e.g., data preprocessing, DM, and post-processing • Not so often used terms: – Knowledge extraction, data archeology • Newest hype: – Business intelligence, knowledge management Course on Data Mining Page 31/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 What is DM Useful for? Increase knowledge to base decision upon E.g., impact on marketing The role and importance of KDD and DM has growed rapidly - and is still growing! But DM is not just marketing... Marketing Database Marketing Data Warehousing KDD & Data Mining Course on Data Mining Page 32/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Potential Applications? • Database analysis and decision support: – Market analysis and management – Risk analysis and management – Fraud detection and management • Other applications: – Web mining – Text mining – etc. Course on Data Mining Page 33/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Example (1) • You are a marketing manager for a cellular telephone company: – Customers receive a free phone (worth 150€) with one-year contract; you pay a sales commission of 250€ per contract – Problem: Turnover (after contract expires) is 25% – Giving a new phone to everyone whose contract is expiring is very expensive – Bringing back a customer after quitting is both difficult and expensive Course on Data Mining Page 34/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Example (1) Yippee! I won't leave! • Three months before a contract expires, predict which customers will leave: – If you want to keep a customer that is predicted to leave, offer them a new phone Course on Data Mining Page 35/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Example (2) Oh, yes! I love my Ferrari! • You are an insurance officer and you should define a suitable monthly payment for an 18-year-old boy who has bough a Ferrari … what to do? Course on Data Mining Page 36/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Example (2) • Analyze all previous customer data and paid compensations data • What is the predicted accident probability based on… – Driver's gender (male/female) and age – Car model and age, place of living – etc. • If the accident probability is higher than on average, set the monthly payment accordingly! Course on Data Mining Page 37/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Example (3) • You are in a foreign country and somebody steals or duplicates your credit card or mobile phone … • Credit card companies … – use historical data to build models of fraudulent behaviour and use data mining to help identify similar instances • Phone companies … – analyze patterns that deviate from an expected norm (destination, duration, etc.) Course on Data Mining Page 38/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Example (4) Excellent surfing experience! • Web access logs can be analyzed for … – discovering customer preferences – improving Web site organization • Similarly … – all kinds of log information analysis – user interface/service adaptation Course on Data Mining Page 39/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Knowledge Discovery Process (1) Learning the domain Creating a target data set Data cleaning/preprocessing Data reduction/projection Choosing the DM task Course on Data Mining Page 40/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Knowledge Discovery Process (2) Choosing the DM algorithm(s) Data mining: Search Pattern evaluation Knowledge presentation Use of discovered knowledge Course on Data Mining Page 41/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Typical KDD Process Time based selection Raw data Operational Database Eval. of interestingness Input data 1 Preprocessing Data mining Cleaned Verified Focused 2 Utilization Postprocessing Results 3 Selected usable patterns Course on Data Mining Page 42/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Utilization Increasing potential to support business decisions Making Decisions End User Data Presentation Visualization Techniques Business Analyst Data Mining Information Discovery Data Analyst Data Exploration Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts OLAP, MDA DBA Data Sources Paper, Files, Information Providers, Database Systems, OLTP Course on Data Mining Page 43/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 The Value Chain Decision • Promote product A in region Z. Knowledge • Mail ads to families of profile P • Cross-sell service B to clients C • A quantity Y of product A is used in region Z • Customers of class Y use x% of C during period D Information • X lives in Z Data • Customer data • S is Y years old • X and S moved • W has money in Z • Store data • Demographical Data • Geographical data Course on Data Mining Page 44/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Data Mining Views • General approaches: – Descriptive data mining: • Describe what interesting can be found in this data! • Explain this data to me! – Predictive data mining: • Based on this and previous data, tell me what will happen in the future! • Show me the future trends! Course on Data Mining Page 45/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Data Mining Views • Views based on … – Databases to be mined – Knowledge to be discovered – Techniques utilized – Applications adapted • Let's take a closer look at these views... Course on Data Mining Page 46/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Data Mining Views Databases to be mined Databases • • • • • • • Relational Transactional Object-oriented Object-relational Active Spatial Time-series • • • • • • • Text, XML Multi-media Heterogeneous Legacy Inductive WWW etc. Course on Data Mining Page 47/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Data Mining Views Knowledge to be mined = tasks Knowledge • Characterization = • Discrimination task • • • • Association Classification Clustering Trend • Deviation analysis • Outlier analysis • etc. Course on Data Mining Page 48/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Data Mining Views Techniques utilized • Techniques • • • • • • Database-oriented Data warehouse (OLAP) Machine learning Statistics Visualization Neural networks Etc. Course on Data Mining Page 49/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Data Mining Views Applications adapted Applic. • Retail (supermarkets etc.) • Telecom • Banking • Fraud analysis • DNA mining • Stock market analysis • Web mining • Log data analysis • etc. Course on Data Mining Page 50/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Major Issues in Data Mining • Mining methodologies and interaction: – – – – – – – Mining different kinds of knowledge Interactive mining of knowledge Incorporation of background knowledge DM query languages and ad-hoc DM Visualization of DM results Handling noise and incomplete data The interestingness problem • Performance and scalability: – Efficiency and scalability of DM algorithms – Parallel, distributed and incremental mining methods Course on Data Mining Page 51/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Major Issues in Data Mining • Diversity of data types: – Handling complex types of data – Mining information from heterogeneous databases (Web etc.) • Application and integration of discovered knowledge: – Domain-specific DM tools – Intelligent query answering and decision making – Integration of discovered knowledge with existing knowledge • Protection of data … – Security – Integrity – Privacy Course on Data Mining Page 52/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Historical Data Mining Activities • • • • • • 1989 IJCAI Workshop 1991-1994 KDD Workshops 1995-1998 KDD Conferences 1998 ACM SIGKDD 1999- SIGKDD Conferences And many smaller/new DM conferences … – PAKDD, PKDD – SIAM-Data Mining, (IEEE) ICDM – etc. Course on Data Mining Page 53/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Useful References on Data Mining “Standards” • DM: Conferences: Journals: KDD, PKDD, PAKDD, ... Data Mining and Knowledge Discovery, CACM • DM/DB: Conferences: ACM-SIGMOD/PODS, VLDB, ... Journals: ACM-TODS, J. ACM, IEEE-TKDE, JIIS, ... Conferences: Journals: Machine Learning, AAAI, IJCAI, ... Machine Learning, Artific. Intell., ... • AI/ML: Course on Data Mining Page 54/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Conclusions • Data mining: semi-automatic discovery of interesting patterns from large data sets • Knowledge discovery is a process: – Preprocessing – Data mining – Postprocessing • To be mined, used or utilized different … – – – – Databases (relational, object-oriented, spatial, WWW, …) Knowledge (characterization, clustering, association, …) Techniques (machine learning, statistics, visualization, …) Applications (retail, telecom, Web mining, log analysis, …) Course on Data Mining Page 55/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Conclusions • Module/Week 1: – – – – • What is Data Mining? Association rules 24.10. normal lecture by Mika 26.10. normal lecture by Mika Module/Week 2: – Episode rules, minimal occurrences – 31.10. normal lecture by Mika – 2.11. seminar like lecture by Pirjo • Module/Week 3: – Text mining – 7.11. normal lecture by Mika – 9.11. seminar like lecture by Mika Course on Data Mining Page 56/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Conclusions • Module/Week 4: – Clustering, Classification, Similarity – 14.11. normal lecture by Pirjo – 16.11. seminal like lecture by Mika • Module/Week 5: – – – – • Knowledge discovery process Pre- and postprocessing 21.11. normal lecture by Pirjo 23.11. Seminar like lecture by Pirjo Module/Week 6: – Data mining tools, Summary, Future – 28.11. normal lecture by Pirjo – 30.11. seminal like lecture by Pirjo Course on Data Mining Page 57/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Seminar Presentations • Seminar presentations: – Articles are given on previous week's Wed – Presentation in an HTML page (around 3-5 printed pages) due to seminar starting: • Can be either a HTML page or a printable document in PostScript/PDF format – 30 minutes of presentation – 5-15 minutes of discussion – Active participation Course on Data Mining Page 58/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Seminar Presentations/Groups 1-2 Quantitative Rules MINERULE Course on Data Mining Page 59/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Seminar 1/2: Quantitative Rules • R. Srikant, R. Agrawal: "Mining Quantitative Association Rules in Large Relational Tables", Proc. of the ACM-SIGMOD 1996 Conference on Management of Data, Montreal, Canada, June 1996. Course on Data Mining Page 60/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Seminar 2/2: MINERULE • Rosa Meo, Giuseppe Psaila, Stefano Ceri: "A New SQL-like Operator for Mining Association Rules". VLDB 1996: 122-133 Course on Data Mining Page 61/62 Mika Klemettinen and Pirjo Moen University of Helsinki/Dept of CS Autumn 2001 Introduction to Data Mining (DM) Thank you for your attention and have a nice course! Thanks to Jiawei Han from Simon Fraser University for his slides which greatly helped in preparing this lecture! Also thanks to Fosca Giannotti and Dino Pedreschi from Pisa for their slides. Course on Data Mining Page 62/62