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Data Mining: Concepts and Techniques — Chapter 1 — Richong Zhang Office: New Main Data Mining: Concepts and Techniques — Chapter 1 — Richong Zhang Office: New Main Building, G 521 Email: [email protected] buaa. edu. cn This slide is made based on the slides provided by Jiawei Han, Micheline Kamber, and Jian Pei. © 2012 Han, Kamber & Pei. 1 1

Course Information Office hour: Thursday 14: 00 -16: 00 Course web site: act. buaa. Course Information Office hour: Thursday 14: 00 -16: 00 Course web site: act. buaa. edu. cn/zhangrc/datamining Course Organization • The first part of the course will be an introduction by the instructor • The second part of the course will have teams of two students presenting relevant research papers (recent three year papers from ICDM and KDD or review papers from reputable journals, such as ACM Trans on Knowledge Discovery from Data or IEEE Trans on Knowledge and Data Engineering) • The final part of the course will be presenting their research projects on topics of data mining. Workload and Assessment: • • Presentation of papers on a topic of data mining (with one or two-page handout/summary): 20% A comprehensive survey (written report, 6 pages, IEEE double column) 20% Class participation: 10% Term-long research project which is considered as the take-home final exam for the course and consists of two components: (a) project presentation: 20% and (b) written project report (written report, 6 pages, IEEE double column): 30%. This report is potentially to be published in a reputable conference/journal. 17 March 2018 Data Mining: Concepts and Techniques 2

Course Information Important Dates: Oct. 20: Choice of at least three papers for the Course Information Important Dates: Oct. 20: Choice of at least three papers for the comprehensive survey, due by 12: 00 noon. Nov. 15: Two-pages project proposal due by 12: 00 noon. Jan. 11: Written project report due by 12: 00 noon. 17 March 2018 Data Mining: Concepts and Techniques 3

Coverage (Chapters 1 -10, 3 rd Ed. ) 1. 2. 3. 4. 5. 6. Coverage (Chapters 1 -10, 3 rd Ed. ) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Introduction Getting to Know Your Data Preprocessing Data Warehouse and OLAP Technology: An Introduction Advanced Data Cube Technology Mining Frequent Patterns & Association: Basic Concepts Mining Frequent Patterns & Association: Advanced Methods Classification: Basic Concepts Classification: Advanced Methods Cluster Analysis: Basic Concepts Data Mining: Cluster Analysis: Advanced Methods Concepts and Techniques, 3 rd ed. Jiawei Han, Micheline Kamber and Jian Pei Available at www. chinapub. com or http: //product. chinapub. com/3683062 4

More Advanced Topics n n n Mining data streams, time-series, and sequence data (Temporal/Spatial More Advanced Topics n n n Mining data streams, time-series, and sequence data (Temporal/Spatial Data Mining) Mining graph data Mining social and information networks Mining object, spatial, multimedia, text and Web data n Mining complex data objects n Spatial and spatiotemporal data mining n Multimedia data mining n Text and Web mining Additional (often current) themes if time permits many real-world applications of data mining 5

A comprehensive survey on a focused topic Possible topics: 1. Stream data mining 2. A comprehensive survey on a focused topic Possible topics: 1. Stream data mining 2. Sequential pattern mining, sequence classification and clustering 3. Time-series analysis, regression and trend analysis 4. Graph pattern mining, graph classification and clustering 5. Social network analysis 6. Spatial, spatiotemporal data mining 7. Multimedia data mining 8. Text mining 9. Mining software programs 10. Statistical data mining methods 11. Other possible topics, which needs to be approved by the instructor 17 March 2018 Data Mining: Concepts and Techniques 6

Course Projects n Finish a related (to the survey) problem as the final project Course Projects n Finish a related (to the survey) problem as the final project n Choose a data set/data sets from public dataset collections and propose a research topic on this data set n http: //snap. stanford. edu/data/ n http: //www. kdnuggets. com/ n n n The code should of your implementation should be submitted together with your finial paper. The topic of your survey paper should be submitted to [email protected] buaa. edu. cn before Oct. 20 st. The proposal (2 pages) of your finial project should be submitted to the above email address before Nov. 15 st. Examples of topics (need to be focused and specific) n Discovering Communities from Flickr Users n Mining Temporal/Spatial Patterns from User-generated Photos 7

Written Reports (Survey and Project) n Introduction of the topic n n n Background Written Reports (Survey and Project) n Introduction of the topic n n n Background Motivation Summarization/Limitation of existing works The contribution of this work Related works n n The advantages and disadvantages of these works n n Choose at least three related areas/problems to be investigated What is the difference between your approach and the existing works Problem formulation/Models/Algorithms n n Notations/Equations/Theories/Inference/Algorithms Empirical studies/Evaluation n n Evaluation metric n Comparative results n n Data set Discussion Conclusion and Future work 8

Presentations n Choose a topic n n n Presentation n Paper presentations will be Presentations n Choose a topic n n n Presentation n Paper presentations will be in 25 -minute time slots: 20 minutes for presentation followed by 5 minutes for questions and answers. the proposed presentation time here is just tentative for now and may need to be adjusted later on, once the actual size of the class is known. Prepare handouts for your presentation. n n Send me an email by 12: 00 noon on Oct 20, stating your names, the topic you choose as well as your choice of at least 5 papers your are going to read. Send me the proposal of your finial project before Nov. 15 st. I will discuss the topic with students individually and evaluate the feasibility of your proposed approach. I will construct the Schedule of Paper Presentations and post it on the course website by Oct. 25. A handout is a one- or two-page summary of the paper to be presented, which is handed out to the audience right before your presentation. Things to be included n n n n What is the main idea or contribution of the papers (or project)? Is it useful or valuable? Why? What assumptions were made? Are these assumptions reasonable or practical? Are the reported results correct or convincing? How can the results be extended or improved? What applications may arise from the main idea of the paper (or project)? Are there any unclear points in the paper (or project)? 9

Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Multi-Dimensional View of Data Mining n What Kinds of Data Can Be Mined? n What Kinds of Patterns Can Be Mined? n What Kinds of Technologies Are Used? n What Kinds of Applications Are Targeted? n Major Issues in Data Mining n A Brief History of Data Mining and Data Mining Society n Summary 10

Why Data Mining? n The Explosive Growth of Data: from terabytes to petabytes n Why Data Mining? n The Explosive Growth of Data: from terabytes to petabytes n Data collection and data availability n Automated data collection tools, database systems, Web, computerized society n Major sources of abundant data n Business: Web, e-commerce, transactions, stocks, … n Science: Remote sensing, bioinformatics, scientific simulation, … n Society and everyone: news, digital cameras, You. Tube n We are drowning in data, but starving for knowledge! n “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets 11

Why Data Mining n Credit ratings/targeted marketing: n n n Identify likely responders to Why Data Mining n Credit ratings/targeted marketing: n n n Identify likely responders to sales promotions Fraud detection n n Given a database of 100, 000 names, which persons are the least likely to default on their credit cards? Which types of transactions are likely to be fraudulent, given the demographics and transactional history of a particular customer? Customer relationship management: n Which of my customers are likely to be the most loyal, and which are most likely to leave for a competitor? : Data Mining helps extract such information

Data mining n Process of semi-automatically analyzing large databases to find patterns that are: Data mining n Process of semi-automatically analyzing large databases to find patterns that are: n n n valid: hold on new data with some certainity novel: non-obvious to the system useful: should be possible to act on the item understandable: humans should be able to interpret the pattern Also known as Knowledge Discovery in Databases (KDD)

Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Multi-Dimensional View of Data Mining n What Kinds of Data Can Be Mined? n What Kinds of Patterns Can Be Mined? n What Kinds of Technologies Are Used? n What Kinds of Applications Are Targeted? n Major Issues in Data Mining n A Brief History of Data Mining and Data Mining Society n Summary 14

What Is Data Mining? n Data mining (knowledge discovery from data) n Extraction of What Is Data Mining? n Data mining (knowledge discovery from data) n Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data n Alternative names n n Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. Watch out: Is everything “data mining”? n Simple search and query processing n (Deductive) expert systems 15

What is (not) Data Mining? l What is not Data Mining? – Look up What is (not) Data Mining? l What is not Data Mining? – Look up phone number in phone directory – Query a Web search engine for information about “Amazon” l What is Data Mining? – Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area) – Group together similar documents returned by search engine according to their context (e. g. Amazon rainforest, Amazon. com, )

Applications n Banking: loan/credit card approval n n Customer relationship management: n n identify Applications n Banking: loan/credit card approval n n Customer relationship management: n n identify likely responders to promotions Fraud detection: telecommunications, financial transactions n n identify those who are likely to leave for a competitor. Targeted marketing: n n predict good customers based on old customers from an online stream of event identify fraudulent events Manufacturing and production: n automatically adjust knobs when process parameter changes

Applications (continued) n Medicine: disease outcome, effectiveness of treatments n n n Molecular/Pharmaceutical: identify Applications (continued) n Medicine: disease outcome, effectiveness of treatments n n n Molecular/Pharmaceutical: identify new drugs Scientific data analysis: n n analyze patient disease history: find relationship between diseases identify new galaxies by searching for sub clusters Web site/store design and promotion: n find affinity of visitor to pages and modify layout

Knowledge Discovery (KDD) Process n n This is a view from typical database systems Knowledge Discovery (KDD) Process n n This is a view from typical database systems and data Pattern Evaluation warehousing communities Data mining plays an essential role in the knowledge discovery Data Mining process Task-relevant Data Warehouse Selection Data Cleaning Data Integration Databases 19

The KDD process n n Problem fomulation Data collection n Pre-processing: cleaning n n The KDD process n n Problem fomulation Data collection n Pre-processing: cleaning n n subset data: sampling might hurt if highly skewed data feature selection: principal component analysis, heuristic search name/address cleaning, different meanings (annual, yearly), duplicate removal, supplying missing values Transformation: map complex objects e. g. time series data to features e. g. frequency Choosing mining task and mining method: Result evaluation and Visualization: n n n Knowledge discovery is an iterative process

Relationship with other fields n Overlaps with machine learning, statistics, artificial intelligence, databases, visualization Relationship with other fields n Overlaps with machine learning, statistics, artificial intelligence, databases, visualization but more stress on n scalability of number of features and instances stress on algorithms and architectures whereas foundations of methods and formulations provided by statistics and machine learning. automation for handling large, heterogeneous data

Some basic operations n Predictive: n n Regression Classification Collaborative Filtering Descriptive: n n Some basic operations n Predictive: n n Regression Classification Collaborative Filtering Descriptive: n n n Clustering / similarity matching Association rules and variants Deviation detection

Example: A Web Mining Framework n Web mining usually involves n Data cleaning n Example: A Web Mining Framework n Web mining usually involves n Data cleaning n Data integration from multiple sources n Warehousing the data n Data cube construction n Data selection for data mining n Data mining n Presentation of the mining results n Patterns and knowledge to be used or stored into knowledge-base 23

Data Mining in Business Intelligence Increasing potential to support business decisions Decision Making Data Data Mining in Business Intelligence Increasing potential to support business decisions Decision Making Data Presentation Visualization Techniques End User Business Analyst Data Mining Information Discovery Data Analyst Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems DBA 24

KDD Process: A Typical View from ML and Statistics Input Data Pre. Processing Data KDD Process: A Typical View from ML and Statistics Input Data Pre. Processing Data integration Normalization Feature selection Dimension reduction n Data Mining Pattern discovery Association & correlation Classification Clustering Outlier analysis … … Post. Processing Pattern evaluation Pattern selection Pattern interpretation Pattern visualization This is a view from typical machine learning and statistics communities 25

Which View Do You Prefer? n Which view do you prefer? n n n Which View Do You Prefer? n Which view do you prefer? n n n KDD vs. ML/Stat. vs. Business Intelligence Depending on the data, applications, and your focus Data Mining vs. Data Exploration n Business intelligence view n n Warehouse, data cube, reporting but not much mining Business objects vs. data mining tools Supply chain example: mining vs. OLAP vs. presentation tools Data presentation vs. data exploration 26

Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Multi-Dimensional View of Data Mining n What Kinds of Data Can Be Mined? n What Kinds of Patterns Can Be Mined? n What Kinds of Technologies Are Used? n What Kinds of Applications Are Targeted? n Major Issues in Data Mining n A Brief History of Data Mining and Data Mining Society n Summary 27

Multi-Dimensional View of Data Mining n n Data to be mined n Database data Multi-Dimensional View of Data Mining n n Data to be mined n Database data (extended-relational, object-oriented, heterogeneous, legacy), 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) n Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc. n Descriptive vs. predictive data mining n Multiple/integrated functions and mining at multiple levels Techniques utilized n Data-intensive, data warehouse (OLAP), machine learning, statistics, pattern recognition, visualization, high-performance, etc. Applications adapted n Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc. 28

Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Multi-Dimensional View of Data Mining n What Kinds of Data Can Be Mined? n What Kinds of Patterns Can Be Mined? n What Kinds of Technologies Are Used? n What Kinds of Applications Are Targeted? n Major Issues in Data Mining n A Brief History of Data Mining and Data Mining Society n Summary 29

Data Mining: On What Kinds of Data? n Database-oriented data sets and applications n Data Mining: On What Kinds of Data? n Database-oriented data sets and applications n n Relational database, data warehouse, transactional database Advanced data sets and advanced applications n Data streams and sensor data n Time-series data, temporal data, sequence data (incl. bio-sequences) n Structure data, graphs, social networks and multi-linked data n Object-relational databases n Heterogeneous databases and legacy databases n Spatial data and spatiotemporal data n Multimedia database n Text databases n The World-Wide Web 30

Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Multi-Dimensional View of Data Mining n What Kinds of Data Can Be Mined? n What Kinds of Patterns Can Be Mined? n What Kinds of Technologies Are Used? n What Kinds of Applications Are Targeted? n Major Issues in Data Mining n A Brief History of Data Mining and Data Mining Society n Summary 31

Data Mining Function: (1) Generalization n Information integration and data warehouse construction n n Data Mining Function: (1) Generalization n Information integration and data warehouse construction n n Data cube technology n n n Data cleaning, transformation, integration, and multidimensional data model Scalable methods for computing (i. e. , materializing) multidimensional aggregates OLAP (online analytical processing) Multidimensional concept description: Characterization and discrimination n Generalize, summarize, and contrast data characteristics, e. g. , dry vs. wet region 32

Data Mining Function: (2) Association and Correlation Analysis n Frequent patterns (or frequent itemsets) Data Mining Function: (2) Association and Correlation Analysis n Frequent patterns (or frequent itemsets) n n What items are frequently purchased together in your Walmart? Association, correlation vs. causality n A typical association rule n n Diaper Beer [0. 5%, 75%] (support, confidence) Are strongly associated items also strongly correlated? How to mine such patterns and rules efficiently in large datasets? How to use such patterns for classification, clustering, and other applications? 33

Association rules T n n n Milk, cereal Tea, milk Given set T of Association rules T n n n Milk, cereal Tea, milk Given set T of groups of items Example: set of item sets purchased Goal: find all rules on itemsets of the Tea, rice, bread form a-->b such that n n support of a and b > user threshold s conditional probability (confidence) of b given a > user threshold c Example: Milk --> bread Purchase of product A --> service B cereal

Variants n n High confidence may not imply high correlation Use correlations. Find expected Variants n n High confidence may not imply high correlation Use correlations. Find expected support and large departures from that interesting. . n n see statistical literature on contingency tables. Still too many rules, need to prune. . .

Prevalent Interesting n n n Analysts already know about prevalent rules Interesting rules are Prevalent Interesting n n n Analysts already know about prevalent rules Interesting rules are those that deviate from prior expectation Mining’s payoff is in finding surprising Zzzz. . . phenomena 1995 Milk and cereal sell together! 1998 Milk and cereal sell together!

What makes a rule surprising? n Does not match prior expectation n Correlation between What makes a rule surprising? n Does not match prior expectation n Correlation between milk and cereal remains roughly constant over time n Cannot be trivially derived from simpler rules n n n Milk 10%, cereal 10% Milk and cereal 10% … surprising Eggs 10% Milk, cereal and eggs 0. 1% … surprising! Expected 1%

Applications of fast itemset counting Find correlated events: n Applications in medicine: find redundant Applications of fast itemset counting Find correlated events: n Applications in medicine: find redundant tests n Cross selling in retail, banking n Improve predictive capability of classifiers that assume attribute independence n New similarity measures of categorical attributes [Mannila et al, KDD 98]

Data Mining Function: (3) Classification n Classification and label prediction n Construct models (functions) Data Mining Function: (3) Classification n Classification and label prediction n Construct models (functions) based on some training examples n Describe and distinguish classes or concepts for future prediction n Predict some unknown class labels Typical methods n n E. g. , classify countries based on (climate), or classify cars based on (gas mileage) Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, pattern-based classification, logistic regression, … Typical applications: n Credit card fraud detection, direct marketing, classifying stars, diseases, web-pages, … 39

Classification n Given old data about customers and payments, predict new applicant’s loan eligibility. Classification n Given old data about customers and payments, predict new applicant’s loan eligibility. Previous customers Age Salary Profession Location Customer type Classifier Decision rules Salary > 5 L Prof. = Exec New applicant’s data Good/ bad

Classification Example o ca g te l ca ri o ca g te in Classification Example o ca g te l ca ri o ca g te in s ou u c t on s s la c Test Set Training Set Learn Classifier Model

Classification methods n n Goal: Predict class Ci = f(x 1, x 2, . Classification methods n n Goal: Predict class Ci = f(x 1, x 2, . . Xn) Regression: (linear or any other polynomial) n n n a*x 1 + b*x 2 + c = Ci. Nearest neighour Decision tree classifier: divide decision space into piecewise constant regions. Probabilistic/generative models Neural networks: partition by non-linear boundaries

Nearest neighbor n n Define proximity between instances, find neighbors of new instance and Nearest neighbor n n Define proximity between instances, find neighbors of new instance and assign majority class Case based reasoning: when attributes are more complicated than real-valued. • Pros + Fast training • Cons – Slow during application. – No feature selection. – Notion of proximity

Decision trees z Tree where internal nodes are simple decision rules on one or Decision trees z Tree where internal nodes are simple decision rules on one or more attributes and leaf nodes are Salary < 1 M predicted class labels. Prof = teacher Good Bad Age < 30 Bad Good

Data Mining Function: (4) Cluster Analysis n n Unsupervised learning (i. e. , Class Data Mining Function: (4) Cluster Analysis n n 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 45

Clustering n n Unsupervised learning when old data with class labels not available e. Clustering n n Unsupervised learning when old data with class labels not available e. g. when introducing a new product. Group/cluster existing customers based on time series of payment history such that similar customers in same cluster. Key requirement: Need a good measure of similarity between instances. Identify micro-markets and develop policies for each

Applications n Customer segmentation e. g. for targeted marketing n n n Collaborative filtering: Applications n Customer segmentation e. g. for targeted marketing n n n Collaborative filtering: n n n Group/cluster existing customers based on time series of payment history such that similar customers in same cluster. Identify micro-markets and develop policies for each group based on common items purchased Text clustering Compression

Distance functions n n Numeric data: euclidean, manhattan distances Categorical data: 0/1 to indicate Distance functions n n Numeric data: euclidean, manhattan distances Categorical data: 0/1 to indicate presence/absence followed by n n Hamming distance (# dissimilarity) Jaccard coefficients: #similarity in 1 s/(# of 1 s) data dependent measures: similarity of A and B depends on co-occurance with C. Combined numeric and categorical data: n weighted normalized distance:

Clustering methods n Hierarchical clustering n n n agglomerative Vs divisive single link Vs Clustering methods n Hierarchical clustering n n n agglomerative Vs divisive single link Vs complete link Partitional clustering n n n distance-based: K-means model-based: EM density-based:

Example of clustering Example of clustering

Model-based approach n n n People and movies belong to unknown classes Pk = Model-based approach n n n People and movies belong to unknown classes Pk = probability a random person is in class k Pl = probability a random movie is in class l Pkl = probability of a class-k person liking a class-l movie Gibbs sampling: iterate n n Pick a person or movie at random and assign to a class with probability proportional to Pk or Pl Estimate new parameters n Need statistics background to understand details

Partitional methods: K-means n Criteria: minimize sum of square of distance n n n Partitional methods: K-means n Criteria: minimize sum of square of distance n n n Between each point and centroid of the cluster. Between each pair of points in the cluster Algorithm: n n Select initial partition with K clusters: random, first K, K separated points Repeat until stabilization: n Assign each point to closest cluster center n Generate new cluster centers n Adjust clusters by merging/splitting

Collaborative Filtering n n Given database of user preferences, predict preference of new user Collaborative Filtering n n Given database of user preferences, predict preference of new user Example: predict what new movies you will like based on n n your past preferences others with similar past preferences their preferences for the new movies Example: predict what books/CDs a person may want to buy n (and suggest it, or give discounts to tempt customer)

Collaborative recommendation • Possible approaches: • Average vote along columns [Same prediction for all] Collaborative recommendation • Possible approaches: • Average vote along columns [Same prediction for all] • Weight vote based on similarity of likings [Group. Lens]

Cluster-based approaches n External attributes of people and movies to cluster n n Cluster Cluster-based approaches n External attributes of people and movies to cluster n n Cluster people based on movie preferences n n age, gender of people actors and directors of movies. [ May not be available] misses information about similarity of movies Repeated clustering: n n cluster movies based on people, then people based on movies, and repeat ad hoc, might smear out groups

Data Mining Function: (5) Outlier Analysis n Outlier analysis n n Outlier: A data Data Mining Function: (5) Outlier Analysis n Outlier analysis n n 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 n Methods: by product of clustering or regression analysis, … n Useful in fraud detection, rare events analysis 57

Time and Ordering: Sequential Pattern, Trend and Evolution Analysis n n Sequence, trend and Time and Ordering: Sequential Pattern, Trend and Evolution Analysis n n Sequence, trend and evolution analysis n Trend, time-series, and deviation analysis: e. g. , regression and value prediction n Sequential pattern mining n e. g. , first buy digital camera, then buy large SD memory cards n Periodicity analysis n Motifs and biological sequence analysis n Approximate and consecutive motifs n Similarity-based analysis Mining data streams n Ordered, time-varying, potentially infinite, data streams 58

Structure and Network Analysis n n n Graph mining n Finding frequent subgraphs (e. Structure and Network Analysis n n n Graph mining n Finding frequent subgraphs (e. g. , chemical compounds), trees (XML), substructures (web fragments) Information network analysis n Social networks: actors (objects, nodes) and relationships (edges) n e. g. , author networks in CS, terrorist networks n Multiple heterogeneous networks n A person could be multiple information networks: friends, family, classmates, … n Links carry a lot of semantic information: Link mining Web mining n Web is a big information network: from Page. Rank to Google n Analysis of Web information networks n Web community discovery, opinion mining, usage mining, … 59

Evaluation of Knowledge n Are all mined knowledge interesting? n n Some may fit Evaluation of Knowledge n Are all mined knowledge interesting? n n Some may fit only certain dimension space (time, location, …) n n One can mine tremendous amount of “patterns” and knowledge Some may not be representative, may be transient, … Evaluation of mined knowledge → directly mine only interesting knowledge? n Descriptive vs. predictive n Coverage n Typicality vs. novelty n Accuracy n Timeliness n … 60

Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Multi-Dimensional View of Data Mining n What Kinds of Data Can Be Mined? n What Kinds of Patterns Can Be Mined? n What Kinds of Technologies Are Used? n What Kinds of Applications Are Targeted? n Major Issues in Data Mining n A Brief History of Data Mining and Data Mining Society n Summary 61

Data Mining: Confluence of Multiple Disciplines Machine Learning Applications Algorithm Pattern Recognition Data Mining Data Mining: Confluence of Multiple Disciplines Machine Learning Applications Algorithm Pattern Recognition Data Mining Database Technology Statistics Visualization High-Performance Computing 62

Why Confluence of Multiple Disciplines? n Tremendous amount of data n n High-dimensionality of Why Confluence of Multiple Disciplines? n Tremendous amount of data n n High-dimensionality of data n n Micro-array may have tens of thousands of dimensions High complexity of data n n n n Algorithms must be highly scalable to handle such as tera-bytes of data Data streams and sensor data Time-series data, temporal data, sequence data Structure data, graphs, social networks and multi-linked data Heterogeneous databases and legacy databases Spatial, spatiotemporal, multimedia, text and Web data Software programs, scientific simulations New and sophisticated applications 63

Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Multi-Dimensional View of Data Mining n What Kinds of Data Can Be Mined? n What Kinds of Patterns Can Be Mined? n What Kinds of Technologies Are Used? n What Kinds of Applications Are Targeted? n Major Issues in Data Mining n A Brief History of Data Mining and Data Mining Society n Summary 64

Applications of Data Mining n Web page analysis: from web page classification, clustering to Applications of Data Mining n Web page analysis: from web page classification, clustering to Page. Rank & HITS algorithms n Collaborative analysis & recommender systems n Basket data analysis to targeted marketing n n n Biological and medical data analysis: classification, cluster analysis (microarray data analysis), biological sequence analysis, biological network analysis Data mining and software engineering (e. g. , IEEE Computer, Aug. 2009 issue) From major dedicated data mining systems/tools (e. g. , SAS, MS SQLServer Analysis Manager, Oracle Data Mining Tools) to invisible data mining 65

Data Mining in Practice 66 Data Mining in Practice 66

Application Areas Industry Finance Insurance Telecommunication Transport Consumer goods Data Service providers Utilities Application Application Areas Industry Finance Insurance Telecommunication Transport Consumer goods Data Service providers Utilities Application Credit Card Analysis Claims, Fraud Analysis Call record analysis Logistics management promotion analysis Value added data Power usage analysis

Why Now? n n n Data is being produced Data is being warehoused The Why Now? n n n Data is being produced Data is being warehoused The computing power is available The computing power is affordable The competitive pressures are strong Commercial products are available

Data Mining works with Warehouse Data n Data Warehousing provides the Enterprise with a Data Mining works with Warehouse Data n Data Warehousing provides the Enterprise with a memory ÑData Mining provides the Enterprise with intelligence

Usage scenarios n Data warehouse mining: n n n assimilate data from operational sources Usage scenarios n Data warehouse mining: n n n assimilate data from operational sources mine static data Mining log data Continuous mining: example in process control Stages in mining: n data selection pre-processing: cleaning transformation mining result evaluation visualization

Vertical integration: Mining on the web n Web log analysis for site design: n Vertical integration: Mining on the web n Web log analysis for site design: n n n what are popular pages, what links are hard to find. Electronic stores sales enhancements: n n n recommendations, advertisement: Collaborative filtering: Net perception, Wisewire Inventory control: what was a shopper looking for and could not find. .

Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Multi-Dimensional View of Data Mining n What Kinds of Data Can Be Mined? n What Kinds of Patterns Can Be Mined? n What Kinds of Technologies Are Used? n What Kinds of Applications Are Targeted? n Major Issues in Data Mining n A Brief History of Data Mining and Data Mining Society n Summary 72

Major Issues in Data Mining (1) n Mining Methodology n n Mining knowledge in Major Issues in Data Mining (1) n Mining Methodology n n Mining knowledge in multi-dimensional space n Data mining: An interdisciplinary effort n Boosting the power of discovery in a networked environment n Handling noise, uncertainty, and incompleteness of data n n Mining various and new kinds of knowledge Pattern evaluation and pattern- or constraint-guided mining User Interaction n Interactive mining n Incorporation of background knowledge n Presentation and visualization of data mining results 73

Major Issues in Data Mining (2) n Efficiency and Scalability n n n Efficiency Major Issues in Data Mining (2) n Efficiency and Scalability n n n Efficiency and scalability of data mining algorithms Parallel, distributed, stream, and incremental mining methods Diversity of data types n n n Handling complex types of data Mining dynamic, networked, and global data repositories Data mining and society n Social impacts of data mining n Privacy-preserving data mining n Invisible data mining 74

Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Multi-Dimensional View of Data Mining n What Kinds of Data Can Be Mined? n What Kinds of Patterns Can Be Mined? n What Kinds of Technologies Are Used? n What Kinds of Applications Are Targeted? n Major Issues in Data Mining n A Brief History of Data Mining and Data Mining Society n Summary 75

A Brief History of Data Mining Society n 1989 IJCAI Workshop on Knowledge Discovery A Brief History of Data Mining Society n 1989 IJCAI Workshop on Knowledge Discovery in Databases n n 1991 -1994 Workshops on Knowledge Discovery in Databases n n Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) 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) n Journal of Data Mining and Knowledge Discovery (1997) n ACM SIGKDD conferences since 1998 and SIGKDD Explorations n More conferences on data mining n n PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), WSDM (2008), etc. ACM Transactions on KDD (2007) 76

Conferences and Journals on Data Mining n KDD Conferences n n ACM SIGKDD Int. Conferences and Journals on Data Mining n KDD Conferences n n ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining (KDD) n SIAM Data Mining Conf. (SDM) n (IEEE) Int. Conf. on Data Mining (ICDM) n European Conf. on Machine Learning and Principles and n practices of Knowledge Discovery and Data Mining (ECML-PKDD) n Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD) n Int. Conf. on Web Search and Data Mining (WSDM) Other related conferences n n DB conferences: ACM SIGMOD, VLDB, ICDE, EDBT, ICDT, … Web and IR conferences: WWW, SIGIR, WSDM n ML conferences: ICML, NIPS n PR conferences: CVPR, Journals n n Data Mining and Knowledge Discovery (DAMI or DMKD) IEEE Trans. On Knowledge and Data Eng. (TKDE) n KDD Explorations n ACM Trans. on KDD 77

Where to Find References? DBLP, Cite. Seer, Google n Data mining and KDD (SIGKDD: Where to Find References? DBLP, Cite. Seer, Google n Data mining and KDD (SIGKDD: CDROM) n n n Database systems (SIGMOD: ACM SIGMOD Anthology—CD ROM) n n n Conferences: SIGIR, WWW, CIKM, etc. Journals: WWW: Internet and Web Information Systems, Statistics n n n Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc. Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE -PAMI, etc. Web and IR n n 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 n n Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD Conferences: Joint Stat. Meeting, etc. Journals: Annals of statistics, etc. Visualization n n Conference proceedings: CHI, ACM-SIGGraph, etc. Journals: IEEE Trans. visualization and computer graphics, etc. 78

Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Chapter 1. Introduction n Why Data Mining? n What Is Data Mining? n A Multi-Dimensional View of Data Mining n What Kinds of Data Can Be Mined? n What Kinds of Patterns Can Be Mined? n What Kinds of Technologies Are Used? n What Kinds of Applications Are Targeted? n Major Issues in Data Mining n A Brief History of Data Mining and Data Mining Society n Summary 79

Summary n n n Data mining: Discovering interesting patterns and knowledge from massive amount Summary n n n 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. n Data mining technologies and applications n Major issues in data mining 80