Uploaded byPriyankapawar886284
64 views

01datamining.pdf

This document provides an introduction to the CSE5243 Intro to Data Mining course at Ohio State University. It outlines the course schedule, textbook, work and grading. The course covers various topics in data mining including what data mining is, why it is used, the types of data and patterns that can be mined, technologies used, applications, and issues. Major data mining functions include classification, clustering, association rule mining, outlier analysis, and analysis of structured/network data.

Embed presentation

Download to read offline
CSE5243 INTRO. TO DATA MINING Chapter 1. Introduction Yu Su, CSE@The Ohio State University Slides adapted from UIUC CS412 by Prof. Jiawei Han and OSU CSE5243 by Prof. Huan Sun
2 CSE 5243. Course Page & Schedule ¨ Class Homepage: https://ysu1989.github.io/courses/sp20/cse5243/ ¨ Class Schedule: 9:35-10:55 AM, Wed/Fri, Caldwell Lab 171 ¨ Office hours: ¤ Instructor: Yu Su @ DL783, Fri 11:00am-12:15pm (right after class) First week: No office hours ¤ TA: Jiaqi Xu (xu.1629), Wed 03:00pm-04:00pm, Baker 406
3 CSE 5243. Textbook ¨ Recommended but not required ¨ (Primary) Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques (3rd ed), 2011 ¤ More resources: https://wiki.illinois.edu/wiki/display/cs412/2.+Course+Syllabus+and +Schedule ¨ (Primary) Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Introduction to Data Mining, 2006 ¨ (Supplementary) Mohammed J. Zaki and Wagner Meira, Jr., Data Mining Analysis and Concepts, 2014 ¨ (Supplementary) Jure Leskovec, Anand Rajaraman, Jeff Ullman, Mining of Massive Datasets ¤ More resources: http://www.mmds.org/
4 CSE 5243. Course Work and Grading ¨ Homework, Course Projects, and Exams ¤ Participation: 10% (Online discussion and/or class participation) ¤ Homework: 50% (No Late Submissions!) ¤ Midterm exam: 20% ¤ Final exam: 20% ¨ Need help and/or discussions? ¤ Carmen: https://osu.instructure.com/courses/76423/discussion_topics n Receive credits: answer questions on Carmen and engage in class discussion. ¨ Check your homework/exam scores ¤ Carmen: https://osu.instructure.com/courses/76423/gradebook
5 Videos ¨ 10 TED talks on Big Data and Analytics ¤ https://www.promptcloud.com/blog/top-ted-talks-on-big-data/ ¤ Shyan Sanker (Director at Palantir Technologies): n https://www.youtube.com/watch?time_continue=19&v=ltelQ3iKybU ¨ 5 TED talks on Data analytics for business leaders ¤ https://bigdata-madesimple.com/5-best-ted-talks-on-data-analytics-for-business- leaders/ ¨ Data analytics for beginners ¤ https://www.youtube.com/watch?v=66ko_cWSHBU (If you love sports, this TED Talk on data analytics is going to be an interesting watch)
6 Chapter 1. Introduction ¨ What is Data Mining? ¨ Why Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
7 What is Data Mining? ¨ Data mining (knowledge discovery from data, KDD) ¤ Extraction of interesting (non-trivial, implicit, previously unknown, and potentially useful) patterns or knowledge from huge amount of data ¨ Alternative names ¤ Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.
8 What is Data Mining? ¨ Data mining (knowledge discovery from data, KDD) ¤ Extraction of interesting (non-trivial, implicit, previously unknown, and potentially useful) patterns or knowledge from huge amount of data ¨ Alternative names ¤ Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. One of the best conferences to publish your research work: SIGKDD (check resources)
9 Knowledge Discovery (KDD) Process ¨ (Narrow view) Data mining plays an essential role in the knowledge discovery process ¨ (Broad view) Data mining is the knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation
10 Example: A Web Mining Framework ¨ Web mining usually involves ¤ Data crawling and cleaning ¤ Data integration from multiple sources ¤ (Optional) Warehousing the data ¤ (Optional) Data cube construction ¤ Data selection for data mining ¤ Data mining ¤ Presentation of the mining results ¤ Patterns and knowledge to be used or stored into knowledge base
12 KDD Process: A View from ML and Statistics ¨ This is a view from typical machine learning and statistics communities Input Data Data Mining Data Pre- Processing Post- Processing Data integration Normalization Feature selection Dimension reduction Pattern discovery Classification Clustering Outlier analysis … … … … Pattern evaluation Pattern selection Pattern interpretation Pattern visualization
13 Data Science Figure from: https://www.datasciencecentral.com/profiles/blogs/difference- of-data-science-machine-learning-and-data-mining
14 Chapter 1. Introduction ¨ What Is Data Mining? ¨ Why Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
15 Why Data Mining? ¨ The Explosive Growth of Data: from terabytes to petabytes ¤ Data collection and data availability n Automated data collection tools, database systems, Web, computerized society
16 Why Data Mining? ¨ The Explosive Growth of Data: from terabytes to petabytes ¤ Data collection and data availability n Automated data collection tools, database systems, Web, computerized society ¤ Major sources of data n Business: Web, e-commerce, transactions, stocks, … n Science: Remote sensing, bioinformatics, scientific simulation, … n Society and everyone: news, digital cameras, YouTube
17 “How much data is generated each day?” – World Economic Forum
18 Why Data Mining? ¨ The Explosive Growth of Data: from terabytes to petabytes ¤ Data collection and data availability n Automated data collection tools, database systems, Web, computerized society ¤ Major sources of data n Business: Web, e-commerce, transactions, stocks, … n Science: Remote sensing, bioinformatics, scientific simulation, … n Society and everyone: news, digital cameras, YouTube ¨ We are drowning in data, but starving for knowledge! ¨ “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets
19 Chapter 1. Introduction ¨ Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
20 Multi-Dimensional View of Data Mining ¨ Data to be mined ¤ Database data (extended-relational, object-oriented, heterogeneous), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks
21 Multi-Dimensional View of Data Mining ¨ Data to be mined ¤ Database data (extended-relational, object-oriented, heterogeneous), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks ¨ Knowledge to be mined (or: Data mining functions) ¤ Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, … ¤ Descriptive vs. predictive data mining ¤ Multiple/integrated functions and mining at multiple levels
22 Multi-Dimensional View of Data Mining ¨ Data to be mined ¤ Database data (extended-relational, object-oriented, heterogeneous), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks ¨ Knowledge to be mined (or: Data mining functions) ¤ Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, … ¤ Descriptive vs. predictive data mining ¤ Multiple/integrated functions and mining at multiple levels ¨ Techniques utilized ¤ Data warehousing (OLAP), machine learning, statistics, pattern recognition, visualization, high-performance computing, etc.
23 Multi-Dimensional View of Data Mining ¨ Data to be mined ¤ Database data (extended-relational, object-oriented, heterogeneous), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks ¨ Knowledge to be mined (or: Data mining functions) ¤ Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, … ¤ Descriptive vs. predictive data mining ¤ Multiple/integrated functions and mining at multiple levels ¨ Techniques utilized ¤ Data warehousing (OLAP), machine learning, statistics, pattern recognition, visualization, high-performance computing, etc. ¨ Applications adapted ¤ Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc.
24 Chapter 1. Introduction ¨ Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
25 Data Mining: On What Kinds of Data? ¨ Database-oriented data sets and applications ¤ Relational database, data warehouse, transactional database ¤ Object-relational databases, Heterogeneous databases and legacy databases ¨ Advanced data sets and advanced applications ¤ Data streams and sensor data ¤ Time-series data, temporal data, sequence data (incl. bio-sequences) ¤ Structure data, graphs, social networks and information networks ¤ Spatial data and spatiotemporal data ¤ Multimedia database ¤ Text databases ¤ The World-Wide Web
26 Survey Your Name, ID, Major Question 1: What do you think Data Mining is? Question 2: What project have you done so far that you think is most relevant to Data Mining? • Not necessarily research project; can be your course project or any hackathon event you participated in. Question 3: What do you expect to learn from this course? Briefly answer each question with a few sentences.
27 Chapter 1. Introduction ¨ Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
28 Data Mining Functions: Pattern Discovery ¨ Frequent patterns ¤ What items do you frequently purchase together on Amazon?
29 Data Mining Functions: Pattern Discovery ¨ Frequent patterns ¤ What items do you frequently purchase together on Amazon? ¨ Association and Correlation Analysis
30 Data Mining Functions: Pattern Discovery ¨ Frequent patterns ¤ What items do you frequently purchase together on Amazon? ¨ Association and Correlation Analysis q A typical association rule q Diaper à Beer [0.5%, 75%] (support, confidence) q How to mine such patterns and rules efficiently in large datasets? q How to use such patterns for classification, clustering, and other applications? q More: friend recommendation, motif discovery, malware detection, fraud detection, etc.
31 Data Mining Functions: Classification ¨ Classification and label prediction ¤ Construct models (functions) based on some training examples ¤ Describe and distinguish classes or concepts for future prediction n Ex. 1. Classify countries based on (climate) n Ex. 2. Classify cars based on (gas mileage) ¤ Predict some unknown class labels
32 Data Mining Functions: Classification ¨ Classification and label prediction ¤ Construct models (functions) based on some training examples ¤ Describe and distinguish classes or concepts for future prediction n Ex. 1. Classify countries based on (climate) n Ex. 2. Classify cars based on (gas mileage) ¤ Predict some unknown class labels ¨ Typical methods ¤ Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, pattern- based classification, logistic regression, …
33 Data Mining Functions: Classification ¨ Classification and label prediction ¤ Construct models (functions) based on some training examples ¤ Describe and distinguish classes or concepts for future prediction n Ex. 1. Classify countries based on (climate) n Ex. 2. Classify cars based on (gas mileage) ¤ Predict some unknown class labels ¨ Typical methods ¤ Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, pattern- based classification, logistic regression, … ¨ Typical applications: ¤ Credit card fraud detection, direct marketing, classifying stars, diseases, web pages, …
34 Data Mining Functions: Cluster Analysis ¨ Unsupervised learning (i.e., Class label is unknown) ¨ Group data to form new categories (i.e., clusters), e.g., cluster houses to find distribution patterns
35 Data Mining Functions: Cluster Analysis ¨ Unsupervised learning (i.e., Class label is unknown) ¨ Group data to form new categories (i.e., clusters), e.g., cluster houses to find distribution patterns ¨ Principle: Maximizing intra-class similarity & minimizing interclass similarity ¨ Many methods and applications
36 Data Mining Functions: Outlier Analysis ¨ Outlier analysis ¤ Outlier: A data object that does not comply with the general behavior of the data ¤ Noise or exception?―One person’s garbage could be another person’s treasure
37 Data Mining Functions: Outlier Analysis ¨ Outlier analysis ¤ Outlier: A data object that does not comply with the general behavior of the data ¤ Noise or exception?―One person’s garbage could be another person’s treasure ¤ Methods: by product of clustering or regression analysis, … ¤ Useful in fraud detection, rare events analysis
38 Data Mining Functions: Time and Ordering: Sequential Pattern, Trend and Evolution Analysis ¨ Sequence, trend and evolution analysis ¤ Trend, time-series, and deviation analysis n e.g., regression and value prediction ¤ Sequential pattern mining n e.g., buy digital camera, then buy large memory cards ¤ Periodicity analysis ¤ Motifs and biological sequence analysis n Approximate and consecutive motifs ¤ Similarity-based analysis ¨ Mining data streams ¤ Ordered, time-varying, potentially infinite, data streams
39 Data Mining Functions: Structure and Network Analysis ¨ Graph mining ¤ Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments)
40 Data Mining Functions: Structure and Network Analysis ¨ Graph mining ¤ Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments) ¨ Information network analysis ¤ Social networks: actors (objects, nodes) and relationships (edges) n e.g., author networks in CS, terrorist networks ¤ Multiple heterogeneous networks n A person could be multiple information networks: friends, family, classmates, … ¤ Knowledge graphs: knowledge backbone of AI systems
41 Data Mining Functions: Structure and Network Analysis ¨ Graph mining ¤ Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments) ¨ Information network analysis ¤ Social networks: actors (objects, nodes) and relationships (edges) n e.g., author networks in CS, terrorist networks ¤ Multiple heterogeneous networks n A person could be multiple information networks: friends, family, classmates, … ¤ Knowledge graphs: knowledge backbone of AI systems ¨ Web mining ¤ Web is a big information network: from PageRank to Google ¤ Analysis of Web information networks n Web community discovery, opinion mining, usage mining, …
42 Evaluation of Knowledge ¨ Are all mined knowledge interesting? ¤ One can mine tremendous amounts of “patterns” ¤ Some may fit only certain dimension space (time, location, …) ¤ Some may not be representative, may be transient, …
43 Evaluation of Knowledge ¨ Are all mined knowledge interesting? ¤ One can mine tremendous amount of “patterns” ¤ Some may fit only certain dimension space (time, location, …) ¤ Some may not be representative, may be transient, … ¨ Evaluation of mined knowledge → directly mine only interesting knowledge? ¤ Descriptive vs. predictive ¤ Coverage ¤ Typicality vs. novelty ¤ Accuracy ¤ Timeliness ¤ …
44 Chapter 1. Introduction ¨ Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
45 Data Mining: Confluence of Multiple Disciplines Data Mining Machine Learning Statistics Applications Algorithm Pattern Recognition High-Performance Computing Visualization Database Technology
46 Why Confluence of Multiple Disciplines? ¨ Tremendous amount of data ¤ Algorithms must be scalable to handle big data ¨ High-dimensionality of data ¤ Micro-array may have tens of thousands of dimensions ¨ High complexity of data ¤ Data streams and sensor data ¤ Time-series data, temporal data, sequence data ¤ Structure data, graphs, social and information networks ¤ Spatial, spatiotemporal, multimedia, text and Web data ¤ Software programs, scientific simulations ¨ New and sophisticated applications
47 Chapter 1. Introduction ¨ Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
48 Applications of Data Mining ¨ Web page analysis: classification, clustering, ranking ¨ Collaborative analysis & recommender systems ¨ Biological and medical data analysis ¨ Data mining and software engineering ¨ Data mining and text analysis ¨ Data mining and social and information network analysis ¨ Built-in (invisible data mining) functions in Google, MS, Yahoo!, Linked, Facebook, …
49 Chapter 1. Introduction ¨ Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
50 Major Issues in Data Mining (1) ¨ Mining Methodology ¤ Mining various and new kinds of knowledge ¤ Mining knowledge in multi-dimensional space ¤ Data mining: An interdisciplinary effort ¤ Boosting the power of discovery in a networked environment ¤ Handling noise, uncertainty, and incompleteness of data ¤ Pattern evaluation and pattern- or constraint-guided mining
51 Major Issues in Data Mining (1) ¨ Mining Methodology ¤ Mining various and new kinds of knowledge ¤ Mining knowledge in multi-dimensional space ¤ Data mining: An interdisciplinary effort ¤ Boosting the power of discovery in a networked environment ¤ Handling noise, uncertainty, and incompleteness of data ¤ Pattern evaluation and pattern- or constraint-guided mining ¨ User Interaction & Human-Machine Collaboration ¤ Interactive mining ¤ Incorporation of background knowledge ¤ Presentation and visualization of data mining results
52 ¨ Efficiency and Scalability ¤ Efficiency and scalability of data mining algorithms ¤ Parallel, distributed, stream, and incremental mining methods ¨ Diversity of data types ¤ Handling complex types of data ¤ Mining dynamic, networked, and global data repositories ¨ Data mining and society ¤ Social impacts of data mining ¤ Privacy-preserving data mining Major Issues in Data Mining (2)
53 Chapter 1. Introduction ¨ Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
54 A Brief History of Data Mining Society ¨ 1989 IJCAI Workshop on Knowledge Discovery in Databases ¤ Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) ¨ 1991-1994 Workshops on Knowledge Discovery in Databases ¤ Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996) ¨ 1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’95-98) ¤ Journal of Data Mining and Knowledge Discovery (1997) ¨ ACM SIGKDD conferences since 1998 and SIGKDD Explorations ¨ More conferences on data mining ¤ PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), WSDM (2008), etc. ¨ ACM Transactions on KDD (2007)
55 Conferences and Journals on Data Mining ¨ KDD Conferences ¤ ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining (KDD) ¤ SIAM Data Mining Conf. (SDM) ¤ (IEEE) Int. Conf. on Data Mining (ICDM) ¤ European Conf. on Machine Learning and Principles and practices of Knowledge Discovery and Data Mining (ECML-PKDD) ¤ Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD) ¤ Int. Conf. on Web Search and Data Mining (WSDM) n Other related conferences n DB conferences: ACM SIGMOD, VLDB, ICDE, EDBT, ICDT, … n Web and IR conferences: WWW, SIGIR, WSDM n ML conferences: ICML, NIPS n PR conferences: CVPR, n Journals n Data Mining and Knowledge Discovery (DAMI or DMKD) n IEEE Trans. On Knowledge and Data Eng. (TKDE) n KDD Explorations n ACM Trans. on KDD
56 Where to Find References? DBLP, CiteSeer, Google ¨ Data mining and KDD (SIGKDD) ¤ Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. ¤ Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD ¨ Database systems (SIGMOD) ¤ Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA ¤ Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc. ¨ AI & Machine Learning ¤ Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc. ¤ Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-PAMI, etc.
57 Where to Find References? DBLP, CiteSeer, Google ¨ Web and IR ¤ Conferences: SIGIR, WWW, CIKM, etc. ¤ Journals: WWW: Internet and Web Information Systems ¨ Statistics ¤ Conferences: Joint Stat. Meeting, etc. ¤ Journals: Annals of statistics, etc. ¨ Visualization ¤ Conference proceedings: CHI, ACM-SIGGraph, etc. ¤ Journals: IEEE Trans. visualization and computer graphics, etc.
58 Future of Data Science Figure from: https://www.datasciencecentral.com/profiles/blogs/difference- of-data-science-machine-learning-and-data-mining https://www.youtube.com/watc h?v=hxXIJnjC_HI (Future of Data Science @ Stanford) Related events in OSU: DataFest Hackathon Conduct research in labs
59 Chapter 1. Introduction ¨ Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
60 Summary ¨ Data mining: Discovering interesting patterns and knowledge from massive amount of data ¨ A natural evolution of science and information technology, in great demand, with wide applications ¨ A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation ¨ Mining can be performed in a variety of data ¨ Data mining functionalities: characterization, discrimination, association, classification, clustering, trend and outlier analysis, etc. ¨ Data mining technologies and applications ¨ Major issues in data mining
61 Recommended Reference Books ¨ Charu C. Aggarwal, Data Mining: The Textbook, Springer, 2015 ¨ E. Alpaydin. Introduction to Machine Learning, 2nd ed., MIT Press, 2011 ¨ R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley- Interscience, 2000 ¨ U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001 ¨ J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd ed. , 2011 ¨ T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, 2009 ¨ T. M. Mitchell, Machine Learning, McGraw Hill, 1997 ¨ P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 (2nd ed. 2016) ¨ I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005 ¨ Mohammed J. Zaki and Wagner Meira Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms 2014
62 Future of Data Science Figure from: https://www.datasciencecentral.com/profiles/blogs/difference- of-data-science-machine-learning-and-data-mining https://www.youtube.com/watc h?v=hxXIJnjC_HI Related events in OSU: DataFest Hackathon Conduct research in labs

More Related Content

PPTX
DWDM 3rd EDITION TEXT BOOK SLIDES24.pptx
byKVIJAYKUMAR29
 
PPTX
dataminingintroductionpptpptpptptro.pptx
byNagendraK18
 
PDF
big data and data warehouse unit 1 for college
byCHOLMALUAL
 
PPT
Data Mining and Warehousing Concept and Techniques
byAnilkumarBrahmane2
 
PPT
introduction to datamining and warehousing
byssuser4ab3a2
 
PPT
introduction to data warehousing and mining slides
byhassnataziz1929
 
PPT
DATA MINING: INTRODUCTION TO DATA MINING
byoceanchaudhary2004
 
PPT
01Intro.ppt data mining dahauuehuwhuwrwhrurhuqhuahura
byHendraPerdana7
 
DWDM 3rd EDITION TEXT BOOK SLIDES24.pptx
byKVIJAYKUMAR29
 
dataminingintroductionpptpptpptptro.pptx
byNagendraK18
 
big data and data warehouse unit 1 for college
byCHOLMALUAL
 
Data Mining and Warehousing Concept and Techniques
byAnilkumarBrahmane2
 
introduction to datamining and warehousing
byssuser4ab3a2
 
introduction to data warehousing and mining slides
byhassnataziz1929
 
DATA MINING: INTRODUCTION TO DATA MINING
byoceanchaudhary2004
 
01Intro.ppt data mining dahauuehuwhuwrwhrurhuqhuahura
byHendraPerdana7
 

Similar to 01datamining.pdf

PPT
3RD B.TECH-DATAMINING-INTRODUCTION-UNIT1 .ppt
bydimpuk1
 
PDF
01 intro
byJoonyoungJayGwak
 
PPT
Upstate CSCI 525 Data Mining Chapter 1
byDanWooster1
 
PDF
Transaction data (for association mining like FP-Growth)
bykibreabtsigabu16
 
PPT
01Intro.ppt
byVaibhavGupta447155
 
PPT
01Intro.ppt
byAidaMustapha6
 
PPT
Data Mining: Concepts and Techniques for beginner
byI Putu Adi Pratama
 
PPT
01Introduction to data mining chapter 1.ppt
byadmsoyadm4
 
PPT
Data Mining Intro
byShubhamSamrat5
 
PPT
data mining
byAMITKUMAR202236
 
PPT
Chapter 1. Introduction.ppt
bySubrata Kumer Paul
 
PDF
01IntroDATAMINGINGEXPORECLASS1INTRODUCTIO.pdf
byanhdph252
 
PPT
01Intro.ppt data analytics r language slide 1
byMuhammadjunaidgulMuh1
 
PPT
Unit 1 (Chapter-1) on data mining concepts.ppt
byPadmajaLaksh
 
PPT
01Intro(1).ppt Introduction In computer science
byHaiderAli84963
 
PPT
Chapter 01Intro.ppt full explanation used
bymy123lapto
 
PPTX
Topic(1)-Intro data mining master ALEX.pptx
byYazanMohamed1
 
PPT
hanjia chapter_1.ppt data mining chapter 1
byPing261512
 
PPT
Introduction of Data Mining - Concept and techniques
bySUMANTWACHASUNDAR1
 
PPT
Chapter 1. Introduction
bybutest
 
3RD B.TECH-DATAMINING-INTRODUCTION-UNIT1 .ppt
bydimpuk1
 
01 intro
byJoonyoungJayGwak
 
Upstate CSCI 525 Data Mining Chapter 1
byDanWooster1
 
Transaction data (for association mining like FP-Growth)
bykibreabtsigabu16
 
01Intro.ppt
byVaibhavGupta447155
 
01Intro.ppt
byAidaMustapha6
 
Data Mining: Concepts and Techniques for beginner
byI Putu Adi Pratama
 
01Introduction to data mining chapter 1.ppt
byadmsoyadm4
 
Data Mining Intro
byShubhamSamrat5
 
data mining
byAMITKUMAR202236
 
Chapter 1. Introduction.ppt
bySubrata Kumer Paul
 
01IntroDATAMINGINGEXPORECLASS1INTRODUCTIO.pdf
byanhdph252
 
01Intro.ppt data analytics r language slide 1
byMuhammadjunaidgulMuh1
 
Unit 1 (Chapter-1) on data mining concepts.ppt
byPadmajaLaksh
 
01Intro(1).ppt Introduction In computer science
byHaiderAli84963
 
Chapter 01Intro.ppt full explanation used
bymy123lapto
 
Topic(1)-Intro data mining master ALEX.pptx
byYazanMohamed1
 
hanjia chapter_1.ppt data mining chapter 1
byPing261512
 
Introduction of Data Mining - Concept and techniques
bySUMANTWACHASUNDAR1
 
Chapter 1. Introduction
bybutest
 

Recently uploaded

PDF
"Master Excel with CA Suvidha Chaplot: 40 Must-Know Tips for Efficiency"
byCA Suvidha Chaplot
 
PPTX
Toxicity symptoms in plants due to higher concentration
byranveer02yadav
 
PPTX
TreatmentPlan of gingival diseases and options
byMohamadSaid29
 
PPTX
Autism types, forms and everything we need to know
byfuzertamas
 
PPTX
7.__Developing_a_Research_Proposal[1].pptx
bynahomeshetu340
 
PDF
Plainte française dans le cadre de l'affaire Epstein
bySociété Tripalio
 
PPTX
Internship ppt about multiple disease prediction
byGOUTHAMTr2
 
PDF
Maricar Payaoan Belarma Portfolio :)))))
bymaricarbelarmava
 
PPTX
maaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
byAshleyJane9
 
PDF
Powering_AI-Ready_MySQL_MyVector_ProxySQL_v3.pptx.pdf
byAlkin Tezuysal
 
PDF
Social Media in Gurgaon: Strategies, Services & Solutions by Bala Infotech
byBala Infotech
 
PDF
What is Context Engineering for Agentforce Developer and Architect
byParis Salesforce Developer Group
 
PDF
Top 10 Excel Tips for Efficiency | Learn Advanced Excel Techniques CA SUVIDHA...
byCA Suvidha Chaplot
 
PPTX
Microsoft-Dynamics-365-Business-Central (1).pptx
byUlirRohwana
 
PDF
제 23회 보아즈(BOAZ) 빅데이터 컨퍼런스 - [MBOAX] : ABSA를 활용한 소비자 반응 분석 기반 운영 효율화 대시보드 설계
byBOAZ Bigdata
 
PPTX
Merge Sort Algorithm: Analysis of Time and Space Complexity.pptx
byrigurahu
 
PPTX
Smart Crop prediction presentation for final year
bySurya386647
 
PPTX
Hacking-Uncovered-How-People-Get-Hacked-and-How-to-Stay-Safe[1].pptx
bylegendgaminghari
 
PDF
SF User Group - Account-Contact Relationship Feb 2026
byLukas Lunow
 
PDF
Macro Energy Trust Shock - current developments in the Venezuelan energy sector
byAurélie Dupassieux
 
"Master Excel with CA Suvidha Chaplot: 40 Must-Know Tips for Efficiency"
byCA Suvidha Chaplot
 
Toxicity symptoms in plants due to higher concentration
byranveer02yadav
 
TreatmentPlan of gingival diseases and options
byMohamadSaid29
 
Autism types, forms and everything we need to know
byfuzertamas
 
7.__Developing_a_Research_Proposal[1].pptx
bynahomeshetu340
 
Plainte française dans le cadre de l'affaire Epstein
bySociété Tripalio
 
Internship ppt about multiple disease prediction
byGOUTHAMTr2
 
Maricar Payaoan Belarma Portfolio :)))))
bymaricarbelarmava
 
maaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
byAshleyJane9
 
Powering_AI-Ready_MySQL_MyVector_ProxySQL_v3.pptx.pdf
byAlkin Tezuysal
 
Social Media in Gurgaon: Strategies, Services & Solutions by Bala Infotech
byBala Infotech
 
What is Context Engineering for Agentforce Developer and Architect
byParis Salesforce Developer Group
 
Top 10 Excel Tips for Efficiency | Learn Advanced Excel Techniques CA SUVIDHA...
byCA Suvidha Chaplot
 
Microsoft-Dynamics-365-Business-Central (1).pptx
byUlirRohwana
 
제 23회 보아즈(BOAZ) 빅데이터 컨퍼런스 - [MBOAX] : ABSA를 활용한 소비자 반응 분석 기반 운영 효율화 대시보드 설계
byBOAZ Bigdata
 
Merge Sort Algorithm: Analysis of Time and Space Complexity.pptx
byrigurahu
 
Smart Crop prediction presentation for final year
bySurya386647
 
Hacking-Uncovered-How-People-Get-Hacked-and-How-to-Stay-Safe[1].pptx
bylegendgaminghari
 
SF User Group - Account-Contact Relationship Feb 2026
byLukas Lunow
 
Macro Energy Trust Shock - current developments in the Venezuelan energy sector
byAurélie Dupassieux
 

01datamining.pdf

  • 1.
    CSE5243 INTRO. TODATA MINING Chapter 1. Introduction Yu Su, CSE@The Ohio State University Slides adapted from UIUC CS412 by Prof. Jiawei Han and OSU CSE5243 by Prof. Huan Sun
  • 2.
    2 CSE 5243. CoursePage & Schedule ¨ Class Homepage: https://ysu1989.github.io/courses/sp20/cse5243/ ¨ Class Schedule: 9:35-10:55 AM, Wed/Fri, Caldwell Lab 171 ¨ Office hours: ¤ Instructor: Yu Su @ DL783, Fri 11:00am-12:15pm (right after class) First week: No office hours ¤ TA: Jiaqi Xu (xu.1629), Wed 03:00pm-04:00pm, Baker 406
  • 3.
    3 CSE 5243. Textbook ¨Recommended but not required ¨ (Primary) Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques (3rd ed), 2011 ¤ More resources: https://wiki.illinois.edu/wiki/display/cs412/2.+Course+Syllabus+and +Schedule ¨ (Primary) Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Introduction to Data Mining, 2006 ¨ (Supplementary) Mohammed J. Zaki and Wagner Meira, Jr., Data Mining Analysis and Concepts, 2014 ¨ (Supplementary) Jure Leskovec, Anand Rajaraman, Jeff Ullman, Mining of Massive Datasets ¤ More resources: http://www.mmds.org/
  • 4.
    4 CSE 5243. CourseWork and Grading ¨ Homework, Course Projects, and Exams ¤ Participation: 10% (Online discussion and/or class participation) ¤ Homework: 50% (No Late Submissions!) ¤ Midterm exam: 20% ¤ Final exam: 20% ¨ Need help and/or discussions? ¤ Carmen: https://osu.instructure.com/courses/76423/discussion_topics n Receive credits: answer questions on Carmen and engage in class discussion. ¨ Check your homework/exam scores ¤ Carmen: https://osu.instructure.com/courses/76423/gradebook
  • 5.
    5 Videos ¨ 10 TEDtalks on Big Data and Analytics ¤ https://www.promptcloud.com/blog/top-ted-talks-on-big-data/ ¤ Shyan Sanker (Director at Palantir Technologies): n https://www.youtube.com/watch?time_continue=19&v=ltelQ3iKybU ¨ 5 TED talks on Data analytics for business leaders ¤ https://bigdata-madesimple.com/5-best-ted-talks-on-data-analytics-for-business- leaders/ ¨ Data analytics for beginners ¤ https://www.youtube.com/watch?v=66ko_cWSHBU (If you love sports, this TED Talk on data analytics is going to be an interesting watch)
  • 6.
    6 Chapter 1. Introduction ¨What is Data Mining? ¨ Why Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
  • 7.
    7 What is DataMining? ¨ Data mining (knowledge discovery from data, KDD) ¤ Extraction of interesting (non-trivial, implicit, previously unknown, and potentially useful) patterns or knowledge from huge amount of data ¨ Alternative names ¤ Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.
  • 8.
    8 What is DataMining? ¨ Data mining (knowledge discovery from data, KDD) ¤ Extraction of interesting (non-trivial, implicit, previously unknown, and potentially useful) patterns or knowledge from huge amount of data ¨ Alternative names ¤ Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. One of the best conferences to publish your research work: SIGKDD (check resources)
  • 9.
    9 Knowledge Discovery (KDD)Process ¨ (Narrow view) Data mining plays an essential role in the knowledge discovery process ¨ (Broad view) Data mining is the knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation
  • 10.
    10 Example: A WebMining Framework ¨ Web mining usually involves ¤ Data crawling and cleaning ¤ Data integration from multiple sources ¤ (Optional) Warehousing the data ¤ (Optional) Data cube construction ¤ Data selection for data mining ¤ Data mining ¤ Presentation of the mining results ¤ Patterns and knowledge to be used or stored into knowledge base
  • 11.
    12 KDD Process: AView from ML and Statistics ¨ This is a view from typical machine learning and statistics communities Input Data Data Mining Data Pre- Processing Post- Processing Data integration Normalization Feature selection Dimension reduction Pattern discovery Classification Clustering Outlier analysis … … … … Pattern evaluation Pattern selection Pattern interpretation Pattern visualization
  • 12.
    13 Data Science Figure from:https://www.datasciencecentral.com/profiles/blogs/difference- of-data-science-machine-learning-and-data-mining
  • 13.
    14 Chapter 1. Introduction ¨What Is Data Mining? ¨ Why Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
  • 14.
    15 Why Data Mining? ¨The Explosive Growth of Data: from terabytes to petabytes ¤ Data collection and data availability n Automated data collection tools, database systems, Web, computerized society
  • 15.
    16 Why Data Mining? ¨The Explosive Growth of Data: from terabytes to petabytes ¤ Data collection and data availability n Automated data collection tools, database systems, Web, computerized society ¤ Major sources of data n Business: Web, e-commerce, transactions, stocks, … n Science: Remote sensing, bioinformatics, scientific simulation, … n Society and everyone: news, digital cameras, YouTube
  • 16.
    17 “How muchdata is generated each day?” – World Economic Forum
  • 17.
    18 Why Data Mining? ¨The Explosive Growth of Data: from terabytes to petabytes ¤ Data collection and data availability n Automated data collection tools, database systems, Web, computerized society ¤ Major sources of data n Business: Web, e-commerce, transactions, stocks, … n Science: Remote sensing, bioinformatics, scientific simulation, … n Society and everyone: news, digital cameras, YouTube ¨ We are drowning in data, but starving for knowledge! ¨ “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets
  • 18.
    19 Chapter 1. Introduction ¨Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
  • 19.
    20 Multi-Dimensional View ofData Mining ¨ Data to be mined ¤ Database data (extended-relational, object-oriented, heterogeneous), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks
  • 20.
    21 Multi-Dimensional View ofData Mining ¨ Data to be mined ¤ Database data (extended-relational, object-oriented, heterogeneous), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks ¨ Knowledge to be mined (or: Data mining functions) ¤ Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, … ¤ Descriptive vs. predictive data mining ¤ Multiple/integrated functions and mining at multiple levels
  • 21.
    22 Multi-Dimensional View ofData Mining ¨ Data to be mined ¤ Database data (extended-relational, object-oriented, heterogeneous), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks ¨ Knowledge to be mined (or: Data mining functions) ¤ Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, … ¤ Descriptive vs. predictive data mining ¤ Multiple/integrated functions and mining at multiple levels ¨ Techniques utilized ¤ Data warehousing (OLAP), machine learning, statistics, pattern recognition, visualization, high-performance computing, etc.
  • 22.
    23 Multi-Dimensional View ofData Mining ¨ Data to be mined ¤ Database data (extended-relational, object-oriented, heterogeneous), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks ¨ Knowledge to be mined (or: Data mining functions) ¤ Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, … ¤ Descriptive vs. predictive data mining ¤ Multiple/integrated functions and mining at multiple levels ¨ Techniques utilized ¤ Data warehousing (OLAP), machine learning, statistics, pattern recognition, visualization, high-performance computing, etc. ¨ Applications adapted ¤ Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc.
  • 23.
    24 Chapter 1. Introduction ¨Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
  • 24.
    25 Data Mining: OnWhat Kinds of Data? ¨ Database-oriented data sets and applications ¤ Relational database, data warehouse, transactional database ¤ Object-relational databases, Heterogeneous databases and legacy databases ¨ Advanced data sets and advanced applications ¤ Data streams and sensor data ¤ Time-series data, temporal data, sequence data (incl. bio-sequences) ¤ Structure data, graphs, social networks and information networks ¤ Spatial data and spatiotemporal data ¤ Multimedia database ¤ Text databases ¤ The World-Wide Web
  • 25.
    26 Survey Your Name, ID,Major Question 1: What do you think Data Mining is? Question 2: What project have you done so far that you think is most relevant to Data Mining? • Not necessarily research project; can be your course project or any hackathon event you participated in. Question 3: What do you expect to learn from this course? Briefly answer each question with a few sentences.
  • 26.
    27 Chapter 1. Introduction ¨Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
  • 27.
    28 Data Mining Functions:Pattern Discovery ¨ Frequent patterns ¤ What items do you frequently purchase together on Amazon?
  • 28.
    29 Data Mining Functions:Pattern Discovery ¨ Frequent patterns ¤ What items do you frequently purchase together on Amazon? ¨ Association and Correlation Analysis
  • 29.
    30 Data Mining Functions:Pattern Discovery ¨ Frequent patterns ¤ What items do you frequently purchase together on Amazon? ¨ Association and Correlation Analysis q A typical association rule q Diaper à Beer [0.5%, 75%] (support, confidence) q How to mine such patterns and rules efficiently in large datasets? q How to use such patterns for classification, clustering, and other applications? q More: friend recommendation, motif discovery, malware detection, fraud detection, etc.
  • 30.
    31 Data Mining Functions:Classification ¨ Classification and label prediction ¤ Construct models (functions) based on some training examples ¤ Describe and distinguish classes or concepts for future prediction n Ex. 1. Classify countries based on (climate) n Ex. 2. Classify cars based on (gas mileage) ¤ Predict some unknown class labels
  • 31.
    32 Data Mining Functions:Classification ¨ Classification and label prediction ¤ Construct models (functions) based on some training examples ¤ Describe and distinguish classes or concepts for future prediction n Ex. 1. Classify countries based on (climate) n Ex. 2. Classify cars based on (gas mileage) ¤ Predict some unknown class labels ¨ Typical methods ¤ Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, pattern- based classification, logistic regression, …
  • 32.
    33 Data Mining Functions:Classification ¨ Classification and label prediction ¤ Construct models (functions) based on some training examples ¤ Describe and distinguish classes or concepts for future prediction n Ex. 1. Classify countries based on (climate) n Ex. 2. Classify cars based on (gas mileage) ¤ Predict some unknown class labels ¨ Typical methods ¤ Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, pattern- based classification, logistic regression, … ¨ Typical applications: ¤ Credit card fraud detection, direct marketing, classifying stars, diseases, web pages, …
  • 33.
    34 Data Mining Functions:Cluster Analysis ¨ Unsupervised learning (i.e., Class label is unknown) ¨ Group data to form new categories (i.e., clusters), e.g., cluster houses to find distribution patterns
  • 34.
    35 Data Mining Functions:Cluster Analysis ¨ Unsupervised learning (i.e., Class label is unknown) ¨ Group data to form new categories (i.e., clusters), e.g., cluster houses to find distribution patterns ¨ Principle: Maximizing intra-class similarity & minimizing interclass similarity ¨ Many methods and applications
  • 35.
    36 Data Mining Functions:Outlier Analysis ¨ Outlier analysis ¤ Outlier: A data object that does not comply with the general behavior of the data ¤ Noise or exception?―One person’s garbage could be another person’s treasure
  • 36.
    37 Data Mining Functions:Outlier Analysis ¨ Outlier analysis ¤ Outlier: A data object that does not comply with the general behavior of the data ¤ Noise or exception?―One person’s garbage could be another person’s treasure ¤ Methods: by product of clustering or regression analysis, … ¤ Useful in fraud detection, rare events analysis
  • 37.
    38 Data Mining Functions:Time and Ordering: Sequential Pattern, Trend and Evolution Analysis ¨ Sequence, trend and evolution analysis ¤ Trend, time-series, and deviation analysis n e.g., regression and value prediction ¤ Sequential pattern mining n e.g., buy digital camera, then buy large memory cards ¤ Periodicity analysis ¤ Motifs and biological sequence analysis n Approximate and consecutive motifs ¤ Similarity-based analysis ¨ Mining data streams ¤ Ordered, time-varying, potentially infinite, data streams
  • 38.
    39 Data Mining Functions:Structure and Network Analysis ¨ Graph mining ¤ Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments)
  • 39.
    40 Data Mining Functions:Structure and Network Analysis ¨ Graph mining ¤ Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments) ¨ Information network analysis ¤ Social networks: actors (objects, nodes) and relationships (edges) n e.g., author networks in CS, terrorist networks ¤ Multiple heterogeneous networks n A person could be multiple information networks: friends, family, classmates, … ¤ Knowledge graphs: knowledge backbone of AI systems
  • 40.
    41 Data Mining Functions:Structure and Network Analysis ¨ Graph mining ¤ Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments) ¨ Information network analysis ¤ Social networks: actors (objects, nodes) and relationships (edges) n e.g., author networks in CS, terrorist networks ¤ Multiple heterogeneous networks n A person could be multiple information networks: friends, family, classmates, … ¤ Knowledge graphs: knowledge backbone of AI systems ¨ Web mining ¤ Web is a big information network: from PageRank to Google ¤ Analysis of Web information networks n Web community discovery, opinion mining, usage mining, …
  • 41.
    42 Evaluation of Knowledge ¨Are all mined knowledge interesting? ¤ One can mine tremendous amounts of “patterns” ¤ Some may fit only certain dimension space (time, location, …) ¤ Some may not be representative, may be transient, …
  • 42.
    43 Evaluation of Knowledge ¨Are all mined knowledge interesting? ¤ One can mine tremendous amount of “patterns” ¤ Some may fit only certain dimension space (time, location, …) ¤ Some may not be representative, may be transient, … ¨ Evaluation of mined knowledge → directly mine only interesting knowledge? ¤ Descriptive vs. predictive ¤ Coverage ¤ Typicality vs. novelty ¤ Accuracy ¤ Timeliness ¤ …
  • 43.
    44 Chapter 1. Introduction ¨Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
  • 44.
    45 Data Mining: Confluenceof Multiple Disciplines Data Mining Machine Learning Statistics Applications Algorithm Pattern Recognition High-Performance Computing Visualization Database Technology
  • 45.
    46 Why Confluence ofMultiple Disciplines? ¨ Tremendous amount of data ¤ Algorithms must be scalable to handle big data ¨ High-dimensionality of data ¤ Micro-array may have tens of thousands of dimensions ¨ High complexity of data ¤ Data streams and sensor data ¤ Time-series data, temporal data, sequence data ¤ Structure data, graphs, social and information networks ¤ Spatial, spatiotemporal, multimedia, text and Web data ¤ Software programs, scientific simulations ¨ New and sophisticated applications
  • 46.
    47 Chapter 1. Introduction ¨Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
  • 47.
    48 Applications of DataMining ¨ Web page analysis: classification, clustering, ranking ¨ Collaborative analysis & recommender systems ¨ Biological and medical data analysis ¨ Data mining and software engineering ¨ Data mining and text analysis ¨ Data mining and social and information network analysis ¨ Built-in (invisible data mining) functions in Google, MS, Yahoo!, Linked, Facebook, …
  • 48.
    49 Chapter 1. Introduction ¨Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
  • 49.
    50 Major Issues inData Mining (1) ¨ Mining Methodology ¤ Mining various and new kinds of knowledge ¤ Mining knowledge in multi-dimensional space ¤ Data mining: An interdisciplinary effort ¤ Boosting the power of discovery in a networked environment ¤ Handling noise, uncertainty, and incompleteness of data ¤ Pattern evaluation and pattern- or constraint-guided mining
  • 50.
    51 Major Issues inData Mining (1) ¨ Mining Methodology ¤ Mining various and new kinds of knowledge ¤ Mining knowledge in multi-dimensional space ¤ Data mining: An interdisciplinary effort ¤ Boosting the power of discovery in a networked environment ¤ Handling noise, uncertainty, and incompleteness of data ¤ Pattern evaluation and pattern- or constraint-guided mining ¨ User Interaction & Human-Machine Collaboration ¤ Interactive mining ¤ Incorporation of background knowledge ¤ Presentation and visualization of data mining results
  • 51.
    52 ¨ Efficiency andScalability ¤ Efficiency and scalability of data mining algorithms ¤ Parallel, distributed, stream, and incremental mining methods ¨ Diversity of data types ¤ Handling complex types of data ¤ Mining dynamic, networked, and global data repositories ¨ Data mining and society ¤ Social impacts of data mining ¤ Privacy-preserving data mining Major Issues in Data Mining (2)
  • 52.
    53 Chapter 1. Introduction ¨Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
  • 53.
    54 A Brief Historyof Data Mining Society ¨ 1989 IJCAI Workshop on Knowledge Discovery in Databases ¤ Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) ¨ 1991-1994 Workshops on Knowledge Discovery in Databases ¤ Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996) ¨ 1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’95-98) ¤ Journal of Data Mining and Knowledge Discovery (1997) ¨ ACM SIGKDD conferences since 1998 and SIGKDD Explorations ¨ More conferences on data mining ¤ PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), WSDM (2008), etc. ¨ ACM Transactions on KDD (2007)
  • 54.
    55 Conferences and Journalson Data Mining ¨ KDD Conferences ¤ ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining (KDD) ¤ SIAM Data Mining Conf. (SDM) ¤ (IEEE) Int. Conf. on Data Mining (ICDM) ¤ European Conf. on Machine Learning and Principles and practices of Knowledge Discovery and Data Mining (ECML-PKDD) ¤ Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD) ¤ Int. Conf. on Web Search and Data Mining (WSDM) n Other related conferences n DB conferences: ACM SIGMOD, VLDB, ICDE, EDBT, ICDT, … n Web and IR conferences: WWW, SIGIR, WSDM n ML conferences: ICML, NIPS n PR conferences: CVPR, n Journals n Data Mining and Knowledge Discovery (DAMI or DMKD) n IEEE Trans. On Knowledge and Data Eng. (TKDE) n KDD Explorations n ACM Trans. on KDD
  • 55.
    56 Where to FindReferences? DBLP, CiteSeer, Google ¨ Data mining and KDD (SIGKDD) ¤ Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. ¤ Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD ¨ Database systems (SIGMOD) ¤ Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA ¤ Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc. ¨ AI & Machine Learning ¤ Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc. ¤ Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-PAMI, etc.
  • 56.
    57 Where to FindReferences? DBLP, CiteSeer, Google ¨ Web and IR ¤ Conferences: SIGIR, WWW, CIKM, etc. ¤ Journals: WWW: Internet and Web Information Systems ¨ Statistics ¤ Conferences: Joint Stat. Meeting, etc. ¤ Journals: Annals of statistics, etc. ¨ Visualization ¤ Conference proceedings: CHI, ACM-SIGGraph, etc. ¤ Journals: IEEE Trans. visualization and computer graphics, etc.
  • 57.
    58 Future of DataScience Figure from: https://www.datasciencecentral.com/profiles/blogs/difference- of-data-science-machine-learning-and-data-mining https://www.youtube.com/watc h?v=hxXIJnjC_HI (Future of Data Science @ Stanford) Related events in OSU: DataFest Hackathon Conduct research in labs
  • 58.
    59 Chapter 1. Introduction ¨Why Data Mining? ¨ What Is Data Mining? ¨ A Multi-Dimensional View of Data Mining ¨ What Kinds of Data Can Be Mined? ¨ What Kinds of Patterns Can Be Mined? ¨ What Kinds of Technologies Are Used? ¨ What Kinds of Applications Are Targeted? ¨ Major Issues in Data Mining ¨ A Brief History of Data Mining and Data Mining Society ¨ Summary
  • 59.
    60 Summary ¨ Data mining:Discovering interesting patterns and knowledge from massive amount of data ¨ A natural evolution of science and information technology, in great demand, with wide applications ¨ A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation ¨ Mining can be performed in a variety of data ¨ Data mining functionalities: characterization, discrimination, association, classification, clustering, trend and outlier analysis, etc. ¨ Data mining technologies and applications ¨ Major issues in data mining
  • 60.
    61 Recommended Reference Books ¨Charu C. Aggarwal, Data Mining: The Textbook, Springer, 2015 ¨ E. Alpaydin. Introduction to Machine Learning, 2nd ed., MIT Press, 2011 ¨ R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley- Interscience, 2000 ¨ U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001 ¨ J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd ed. , 2011 ¨ T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, 2009 ¨ T. M. Mitchell, Machine Learning, McGraw Hill, 1997 ¨ P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 (2nd ed. 2016) ¨ I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005 ¨ Mohammed J. Zaki and Wagner Meira Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms 2014
  • 61.
    62 Future of DataScience Figure from: https://www.datasciencecentral.com/profiles/blogs/difference- of-data-science-machine-learning-and-data-mining https://www.youtube.com/watc h?v=hxXIJnjC_HI Related events in OSU: DataFest Hackathon Conduct research in labs