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COME 448 Data Mining and Knowledge Discovery Textbook: Jiawei Han and Micheline Kamber, Data COME 448 Data Mining and Knowledge Discovery Textbook: Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 3 rd ed. , 2011. 19 March 2018 Data Mining: Concepts and Techniques 1

Data Mining: Concepts and Techniques — Chapter 1 — — Introduction — Authors: Jiawei Data Mining: Concepts and Techniques — Chapter 1 — — Introduction — Authors: Jiawei Han and Micheline Kamber Department of Computer Science University of Illinois at Urbana-Champaign www. cs. uiuc. edu/~hanj © 2006 Jiawei Han and Micheline Kamber. All rights reserved. 19 March 2018 Data Mining: Concepts and Techniques 2

Course Schedule n 19 March 2018 Coverage (BK 3: 3 rd ed. ) 1. Course Schedule n 19 March 2018 Coverage (BK 3: 3 rd ed. ) 1. Introduction 2. Getting to Know Your Data 3. Data Preprocessing 4. Data Warehouse and OLAP Technology: An Introduction 5. Advanced Data Cube Technology 6. Mining Frequent Patterns & Association: Basic Concepts 7. Mining Frequent Patterns & Association: Advanced Methods 8. Classification: Basic Concepts 9. Classification: Advanced Methods 10. Cluster Analysis: Basic Concepts 11. Cluster Analysis: Advanced Methods 12. Outlier Analysis Data Mining: Concepts and Techniques 3

Course Schedule n Other Topics n Mining data streams, time-series, and sequence data n Course Schedule n Other Topics n Mining data streams, time-series, and sequence data n Mining graphs, social networks and multi-relational data n Mining object, spatial, multimedia, text and Web data n n Spatial and spatiotemporal data mining n Multimedia data mining n Text mining n n Mining complex data objects Web mining Applications and trends of data mining n n 19 March 2018 Visual data mining n n Mining business & biological data Data mining and society: Privacy-preserving data mining Additional (often current) themes could be added to the course Data Mining: Concepts and Techniques 4

Chapter 1. Introduction n Motivation: Why data mining? n What is data mining? n Chapter 1. Introduction n Motivation: Why data mining? n What is data mining? n Data Mining: On what kind of data? n Data mining functionality n Are all the patterns interesting? n Classification of data mining systems n Data Mining Task Primitives n Integration of data mining system with a DB and DW System n Major issues in data mining 19 March 2018 Data Mining: Concepts and Techniques 5

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, 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 19 March 2018 Data Mining: Concepts and Techniques 6

Evolution of Sciences: New Data Science Era n Before 1600: Empirical science n 1600 Evolution of Sciences: New Data Science Era n Before 1600: Empirical science n 1600 -1950 s: Theoretical science n n 1950 s-1990 s: Computational science n n n Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding. Over the last 50 years, most disciplines have grown a third, computational branch (e. g. empirical, theoretical, and computational ecology, or physics, or linguistics. ) Computational Science traditionally meant simulation. It grew out of our inability to find closed-form solutions for complex mathematical models. 1990 -now: Data science n The flood of data from new scientific instruments and simulations n The ability to economically store and manage petabytes of data online n The Internet and computing Grid that makes all these archives universally accessible n n n Scientific info. management, acquisition, organization, query, and visualization tasks scale almost linearly with data volumes Data mining is a major new challenge! Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science , Comm. ACM, 45(11): 50 -54, Nov. 2002 7

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 n Alternative names n n Data mining: a misnomer? 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 19 March 2018 Data Mining: Concepts and Techniques 8

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 9

KDD Process: Several Key Steps n Learning the application domain n relevant prior knowledge KDD Process: Several Key Steps n Learning the application domain n relevant prior knowledge and goals of application n Creating a target data set: data selection n Data cleaning and preprocessing: (may take 60% of effort!) n Data reduction and transformation n n Find useful features, dimensionality/variable reduction, invariant representation Choosing functions of data mining n summarization, classification, regression, association, clustering n Choosing the mining algorithm(s) n Data mining: search for patterns of interest n Pattern evaluation and knowledge presentation n n visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge 19 March 2018 Data Mining: Concepts and Techniques 10

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 11

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 selection interpretation visualization This is a view from typical machine learning and statistics communities 12

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 13

Applications of Data Mining n n n n n Data analysis and decision support Applications of Data Mining n n n n n Data analysis and decision support n Market analysis and management n Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation n Risk analysis and management n Forecasting, customer retention, improved underwriting, quality control, competitive analysis n Fraud detection and detection of unusual patterns (outliers) Other Applications n Text mining (news group, email, documents) and Web mining n Stream data mining n Bioinformatics and bio-data analysis Web page analysis: from web page classification, clustering to Page. Rank & HITS algorithms Text mining (news group, email, documents) and Web mining Collaborative analysis & recommender systems Stream data mining 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 SQL-Server Analysis Manager, Oracle Data Mining Tools) to invisible data mining 19 March 2018 Data Mining: Concepts and Techniques 14

Ex. 1: Market Analysis and Management n n Where does the data come from? Ex. 1: Market Analysis and Management n n Where does the data come from? —Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies Target marketing n n n Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc. Determine customer purchasing patterns over time Cross-market analysis—Find associations/co-relations between product sales, & predict based on such association Customer profiling—What types of customers buy what products (clustering or classification) Customer requirement analysis n n n Identify the best products for different groups of customers Predict what factors will attract new customers Provision of summary information n Multidimensional summary reports n Statistical summary information (data central tendency and variation) 19 March 2018 Data Mining: Concepts and Techniques 15

Ex. 2: Corporate Analysis & Risk Management n Finance planning and asset evaluation n Ex. 2: Corporate Analysis & Risk Management n Finance planning and asset evaluation n cash flow analysis and prediction n contingent claim analysis to evaluate assets n cross-sectional and time series analysis (financial-ratio, trend analysis, etc. ) n Resource planning n n summarize and compare the resources and spending Competition n monitor competitors and market directions n group customers into classes and a class-based pricing procedure n set pricing strategy in a highly competitive market 19 March 2018 Data Mining: Concepts and Techniques 16

Ex. 3: Fraud Detection & Mining Unusual Patterns n Approaches: Clustering & model construction Ex. 3: Fraud Detection & Mining Unusual Patterns n Approaches: Clustering & model construction for frauds, outlier analysis n Applications: Health care, retail, credit card service, telecomm. n Auto insurance: ring of collisions n Money laundering: suspicious monetary transactions n Medical insurance n n n Professional patients, ring of doctors, and ring of references Unnecessary or correlated screening tests Telecommunications: phone-call fraud n n Retail industry n n 19 March 2018 Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm Analysts estimate that 38% of retail shrink is due to dishonest employees Anti-terrorism Data Mining: Concepts and Techniques 17

Data Mining: Confluence of Multiple Disciplines Database Technology Machine Learning Pattern Recognition 19 March Data Mining: Confluence of Multiple Disciplines Database Technology Machine Learning Pattern Recognition 19 March 2018 Statistics Data Mining Algorithm Data Mining: Concepts and Techniques Visualization Other Disciplines 18

Data Mining Development • Relational Data Model • SQL • Association Rule Algorithms • Data Mining Development • Relational Data Model • SQL • Association Rule Algorithms • Data Warehousing • Scalability Techniques • Algorithm Design Techniques • Algorithm Analysis • Data Structures • Similarity Measures • Hierarchical Clustering • IR Systems • Imprecise Queries • Textual Data • Web Search Engines • Bayes Theorem • Regression Analysis • EM Algorithm • K-Means Clustering • Time Series Analysis • Neural Networks • Decision Tree Algorithms 19 March 2018 Data Mining: Concepts and Techniques 19

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 20

Data Mining: Classification Schemes n General functionality n n n Descriptive data mining Predictive Data Mining: Classification Schemes n General functionality n n n Descriptive data mining Predictive data mining Different views lead to different classifications n Data view: Kinds of data to be mined n Knowledge view: Kinds of knowledge to be discovered n Method view: Kinds of techniques utilized n Application view: Kinds of applications adapted 19 March 2018 Data Mining: Concepts and Techniques 21

General functionalities of data mining n Descriptive data mining n n Characterize the general General functionalities of data mining n Descriptive data mining n n Characterize the general properties of the data in the database finds patterns in data and user determines which ones are important Predictive data mining n n 19 March 2018 perform inference on the current data to make predictions we know what to predict Data Mining: Concepts and Techniques 22

Data Mining Models and Tasks 19 March 2018 Data Mining: Concepts and Techniques 23 Data Mining Models and Tasks 19 March 2018 Data Mining: Concepts and Techniques 23

Multi-Dimensional View of Data Mining n Data to be mined n n Knowledge to Multi-Dimensional View of Data Mining n Data to be mined n n Knowledge to be mined n n n Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc. Multiple/integrated functions and mining at multiple levels Techniques utilized n n Relational, data warehouse, transactional, stream, objectoriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc. Applications adapted n 19 March 2018 Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc. Data Mining: Concepts and Techniques 24

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 19 March 2018 Data Mining: Concepts and Techniques 25

An Example problem n n All Electronic is a multi branch retail company relational An Example problem n n All Electronic is a multi branch retail company relational tables include n n n 19 March 2018 customer n ID, name, address, age, income, education , sex, m status items n ID, name, brand, category, type, price, place_made, supplier, cost employee n ID, name, department, education, salary branch purchases n trans. ID, item_sold, customer ID, emp_ID, date, time , method_paid, amount Data Mining: Concepts and Techniques 26

 Relational Databases 19 March 2018 Data Mining: Concepts and Techniques 27 Relational Databases 19 March 2018 Data Mining: Concepts and Techniques 27

Data Mining Functionalities (1) n Concept/Class description: Characterization and discrimination n Data can be Data Mining Functionalities (1) n Concept/Class description: Characterization and discrimination n Data can be associated with classes or concepts Generalize, summarize, and contrast data characteristics, e. g. , dry vs. wet regions Example : Allelectronics store n n 19 March 2018 classes of items for sale include n computers, printers concepts of customers: n big. Spenders n Budget. Spenders Data Mining: Concepts and Techniques 28

Data Mining Functionalities (2) n n n Data Characterization Summarization the data of the Data Mining Functionalities (2) n n n Data Characterization Summarization the data of the class under study (target class) in general terms Methods n n SQL queries OLAP roll up -operation n user-controlled data summarization n along a specified dimension attribute oriented induction n without step by step user interraction the output of characterization n n 19 March 2018 pie charts, bar chars, curves, multidimensional data cube, or cross tabs in rule form as characteristic rules Data Mining: Concepts and Techniques 29

Data Mining Functionalities (3) n n Characterization example Description summarizing the characteristics of customers Data Mining Functionalities (3) n n Characterization example Description summarizing the characteristics of customers who spend more than $1000 a year at All. Elecronics n n n 19 March 2018 age, employment, income drill down on any dimension n on occupation view these according to their type of employment Result: profile of customers: n 40 -50 years old n Employed and have excellent credit ratings Data Mining: Concepts and Techniques 30

Data Mining Functionalities (4) n n Data Discrimination Comparing the target class with one Data Mining Functionalities (4) n n Data Discrimination Comparing the target class with one or a set of comparative classes (contrasting classes) n n n these classes can be specified by the use database queries methods and output n n 19 March 2018 similar to those used for characterization include comparative measures to distinguish between the target and contrasting classes Data Mining: Concepts and Techniques 31

Data Mining Functionalities (5) n n Discrimination examples Example 1: Compare the general features Data Mining Functionalities (5) n n Discrimination examples Example 1: Compare the general features of software products n n n whose sales increased by %10 in the last year whose sales decreased by at least %30 during the same period Example 2: Compare two groups of Allelectronics customers n n n I) who shop for computer products regularly n more than two times a month II) who rarely shop for such products n less than three times a year The resulting description: n %80 of I group customers n n n %60 of II group customers n n 19 March 2018 university education ages 20 -40 seniors or young no university degree Data Mining: Concepts and Techniques 32

Data Mining Functionalities (6) Association Analysis n n Assoc. Anal. -- discovery of association Data Mining Functionalities (6) Association Analysis n n Assoc. Anal. -- discovery of association relationships between attribute-value conditions. Such relationships may be expressed in many ways. On common way is through association rules. X => Y n Frequent patterns, association, correlation vs. causality n 19 March 2018 Diaper Beer [0. 5%, 75%] (Correlation or causality? ) Data Mining: Concepts and Techniques 33

Data Mining Functionalities (7) Association Rules Example: Allelectronics customers under study (2%), are 20 Data Mining Functionalities (7) Association Rules Example: Allelectronics customers under study (2%), are 20 to 29 years of age with income of 20 K to 29 K and have purchased a CD player. There is 60% probablity that customer in these age group and income will purchase a CD player. age (X, “ 20. . 29”) ^ income (X, “ 20 K. . 29 K”) => buys (X, “CD changer) % of data instances satisfying all three components of rule [support = 2% confidence = 60% ] % of data instances where hypothesis is satisfied and conclusion is predicted correctly contains(T, “computer”) => contains(T, “software”) [1%, 75%] If a transdaction contains “computer”, there is 75 % chance that it Contains “software” as well. 19 March 2018 Data Mining: Concepts and Techniques 34

Data Mining Functionalities (8) n Summarization n 19 March 2018 Given a data set, Data Mining Functionalities (8) n Summarization n 19 March 2018 Given a data set, summarize important characteristics of the data. n Mean, median, standard deviation, determine statistical distribution, identify most commonly appearing attribute values, etc. Data Mining: Concepts and Techniques 35

Data Mining Functionalities (9) n Classification and Prediction n n Finding models (functions) that Data Mining Functionalities (9) n Classification and Prediction n n Finding models (functions) that describe and distinguish classes or concepts for future prediction E. g. , classify countries based on climate, or classify cars based on gas mileage n Presentation: decision-tree, classification rule, neural network n Prediction: Predict some unknown or missing numerical values 19 March 2018 Data Mining: Concepts and Techniques 36

Data Mining Functionalities (10) n Classification n n From data with known labels, create Data Mining Functionalities (10) n Classification n n From data with known labels, create a classifier that determines which label to apply to a new observation E. g. Identify new loan applicants as low, medium, or high risk based on existing applicant behavior. Low Medium High 19 March 2018 Data Mining: Concepts and Techniques 37

Data Mining Functionalities (11) n Prediction n n 19 March 2018 Given a collection Data Mining Functionalities (11) n Prediction n n 19 March 2018 Given a collection of data with known numeric outputs, create a function that outputs a predicted value from a new set of inputs. E. g. Given gestation time of an animal, predict its maximum life span. Data Mining: Concepts and Techniques 38

Data Mining Functionalities (12) n Cluster analysis n n Class label is unknown: Group Data Mining Functionalities (12) n Cluster analysis n n Class label is unknown: Group data to form new classes, e. g. , cluster houses to find distribution patterns Clustering based on the principle: n n 19 March 2018 maximizing the intra-class similarity and minimizing the interclass similarity Data Mining: Concepts and Techniques 39

Data Mining Functionalities (13) n Clustering n n n 19 March 2018 Identify “natural” Data Mining Functionalities (13) n Clustering n n n 19 March 2018 Identify “natural” groupings in data Unsupervised learning, no predefined groups E. g. Identify movie goers with similar movie watching habits. Data Mining: Concepts and Techniques 40

Data Mining Functionalities (14) n Outlier analysis n Outlier: a data object that does Data Mining Functionalities (14) n Outlier analysis n Outlier: a data object that does not comply with the general behavior of the data n It can be considered as noise or exception but is quite useful in fraud detection, rare events analysis n Trend and evolution analysis n n Sequential pattern mining, periodicity analysis n n Trend and deviation: regression analysis Similarity-based analysis Other pattern-directed or statistical analyses 19 March 2018 Data Mining: Concepts and Techniques 41

Are All the “Discovered” Patterns Interesting? n Data mining may generate thousands of patterns: Are All the “Discovered” Patterns Interesting? n Data mining may generate thousands of patterns: Not all of them are interesting n n Suggested approach: Human-centered, query-based, focused mining Interestingness measures n A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm n Objective vs. subjective interestingness measures n Objective: based on statistics and structures of patterns, e. g. , support, confidence, etc. n Subjective: based on user’s belief in the data, e. g. , unexpectedness, novelty, actionability, etc. 19 March 2018 Data Mining: Concepts and Techniques 42

Find All and Only Interesting Patterns? n Find all the interesting patterns: Completeness n Find All and Only Interesting Patterns? n Find all the interesting patterns: Completeness n Can a data mining system find all the interesting patterns? Do we need to find all of the interesting patterns? n n n Heuristic vs. exhaustive search Association vs. classification vs. clustering Search for only interesting patterns: An optimization problem n Can a data mining system find only the interesting patterns? n Approaches n n 19 March 2018 First general all the patterns and then filter out the uninteresting ones Generate only the interesting patterns—mining query optimization Data Mining: Concepts and Techniques 43

Other Pattern Mining Issues n Precise patterns vs. approximate patterns n Association and correlation Other Pattern Mining Issues n Precise patterns vs. approximate patterns n Association and correlation mining: possible find sets of precise patterns n n n But approximate patterns can be more compact and sufficient How to find high quality approximate patterns? ? Gene sequence mining: approximate patterns are inherent n n How to derive efficient approximate pattern mining algorithms? ? Constrained vs. non-constrained patterns n n 19 March 2018 Why constraint-based mining? What are the possible kinds of constraints? How to push constraints into the mining process? Data Mining: Concepts and Techniques 44

Primitives that Define a Data Mining Task n Task-relevant data n Type of knowledge Primitives that Define a Data Mining Task n Task-relevant data n Type of knowledge to be mined n Background knowledge n Pattern interestingness measurements n Visualization/presentation of discovered patterns 19 March 2018 Data Mining: Concepts and Techniques 45

Primitive 1: Task-Relevant Data n Database or data warehouse name n Database tables or Primitive 1: Task-Relevant Data n Database or data warehouse name n Database tables or data warehouse cubes n Condition for data selection n Relevant attributes or dimensions n Data grouping criteria 19 March 2018 Data Mining: Concepts and Techniques 46

Primitive 2: Types of Knowledge to Be Mined n Characterization n Discrimination n Association Primitive 2: Types of Knowledge to Be Mined n Characterization n Discrimination n Association n Classification/prediction n Clustering n Outlier analysis n Other data mining tasks 19 March 2018 Data Mining: Concepts and Techniques 47

Primitive 3: Background Knowledge n A typical kind of background knowledge: Concept hierarchies n Primitive 3: Background Knowledge n A typical kind of background knowledge: Concept hierarchies n Schema hierarchy n n Set-grouping hierarchy n n E. g. , street < city < province_or_state < country E. g. , {20 -39} = young, {40 -59} = middle_aged Operation-derived hierarchy n email address: [email protected] uiuc. edu login-name < department < university < country n Rule-based hierarchy n 19 March 2018 low_profit_margin (X) <= price(X, P 1) and cost (X, P 2) and (P 1 P 2) < $50 Data Mining: Concepts and Techniques 48

Primitive 4: Pattern Interestingness Measure n Simplicity e. g. , (association) rule length, (decision) Primitive 4: Pattern Interestingness Measure n Simplicity e. g. , (association) rule length, (decision) tree size n Certainty e. g. , confidence, P(A|B) = #(A and B)/ #(B), classification reliability or accuracy, certainty factor, rule strength, rule quality, discriminating weight, etc. n Utility potential usefulness, e. g. , support (association), noise threshold (description) n Novelty not previously known, surprising (used to remove redundant rules, e. g. , Illinois vs. Champaign rule implication support ratio) 19 March 2018 Data Mining: Concepts and Techniques 49

Primitive 5: Presentation of Discovered Patterns n Different backgrounds/usages may require different forms of Primitive 5: Presentation of Discovered Patterns n Different backgrounds/usages may require different forms of representation n n E. g. , rules, tables, crosstabs, pie/bar chart, etc. Concept hierarchy is also important n Discovered knowledge might be more understandable when represented at high level of abstraction n Interactive drill up/down, pivoting, slicing and dicing provide different perspectives to data n Different kinds of knowledge require different representation: association, classification, clustering, etc. 19 March 2018 Data Mining: Concepts and Techniques 50

Why Data Mining Query Language? n Automated vs. query-driven? n n Data mining should Why Data Mining Query Language? n Automated vs. query-driven? n n Data mining should be an interactive process n n n Finding all the patterns autonomously in a database? —unrealistic because the patterns could be too many but uninteresting User directs what to be mined Users must be provided with a set of primitives to be used to communicate with the data mining system Incorporating these primitives in a data mining query language n More flexible user interaction n Foundation for design of graphical user interface n Standardization of data mining industry and practice 19 March 2018 Data Mining: Concepts and Techniques 51

DMQL—A Data Mining Query Language n Motivation n n A DMQL can provide the DMQL—A Data Mining Query Language n Motivation n n A DMQL can provide the ability to support ad-hoc and interactive data mining By providing a standardized language like SQL n n Hope to achieve a similar effect like that SQL has on relational database Foundation for system development and evolution Facilitate information exchange, technology transfer, commercialization and wide acceptance Design n 19 March 2018 DMQL is designed with the primitives described earlier Data Mining: Concepts and Techniques 52

An Example Query in DMQL 19 March 2018 Data Mining: Concepts and Techniques 53 An Example Query in DMQL 19 March 2018 Data Mining: Concepts and Techniques 53

Other Data Mining Languages & Standardization Efforts n Association rule language specifications n n Other Data Mining Languages & Standardization Efforts n Association rule language specifications n n Mine. Rule (Meo Psaila and Ceri’ 96) n n MSQL (Imielinski & Virmani’ 99) Query flocks based on Datalog syntax (Tsur et al’ 98) OLEDB for DM (Microsoft’ 2000) and recently DMX (Microsoft SQLServer 2005) n n n Based on OLE, OLE DB for OLAP, C# Integrating DBMS, data warehouse and data mining DMML (Data Mining Mark-up Language) by DMG (www. dmg. org) n Providing a platform and process structure for effective data mining n Emphasizing on deploying data mining technology to solve business problems 19 March 2018 Data Mining: Concepts and Techniques 54

Integration of Data Mining and Data Warehousing n Data mining systems, DBMS, Data warehouse Integration of Data Mining and Data Warehousing n Data mining systems, DBMS, Data warehouse systems coupling n n On-line analytical mining data n n No coupling, loose-coupling, semi-tight-coupling, tight-coupling integration of mining and OLAP technologies Interactive mining multi-level knowledge n Necessity of mining knowledge and patterns at different levels of abstraction by drilling/rolling, pivoting, slicing/dicing, etc. n Integration of multiple mining functions n 19 March 2018 Characterized classification, first clustering and then association Data Mining: Concepts and Techniques 55

Coupling Data Mining with DB/DW Systems n No coupling—flat file processing, not recommended n Coupling Data Mining with DB/DW Systems n No coupling—flat file processing, not recommended n Loose coupling n n Semi-tight coupling—enhanced DM performance n n Fetching data from DB/DW Provide efficient implement a few data mining primitives in a DB/DW system, e. g. , sorting, indexing, aggregation, histogram analysis, multiway join, precomputation of some stat functions Tight coupling—A uniform information processing environment n 19 March 2018 DM is smoothly integrated into a DB/DW system, mining query is optimized based on mining query, indexing, query processing methods, etc. Data Mining: Concepts and Techniques 56

Architecture: Typical Data Mining System Graphical User Interface Pattern Evaluation Data Mining Engine Knowl Architecture: Typical Data Mining System Graphical User Interface Pattern Evaluation Data Mining Engine Knowl edge. Base Database or Data Warehouse Server data cleaning, integration, and selection Database 19 March 2018 Data World-Wide Other Info Repositories Warehouse Web Data Mining: Concepts and Techniques 57

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 58

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 59

Summary n n n Data mining: Discovering interesting patterns from large amounts of data Summary n n n Data mining: Discovering interesting patterns from large amounts of data A natural evolution of database 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 information repositories Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc. n Data mining systems and architectures n Major issues in data mining 19 March 2018 Data Mining: Concepts and Techniques 60

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), etc. ACM Transactions on KDD starting in 2007 19 March 2018 Data Mining: Concepts and Techniques 61

Conferences and Journals on Data Mining n KDD Conferences n ACM SIGKDD Int. Conf. Conferences and Journals on Data Mining n KDD Conferences 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 Conf. on Principles and practices of Knowledge Discovery and Data Mining (PKDD) n Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD) 19 March 2018 n Other related conferences n n VLDB n (IEEE) ICDE n WWW, SIGIR n n ACM SIGMOD ICML, CVPR, NIPS 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 Data Mining: Concepts and Techniques 62

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 19 March 2018 Conference proceedings: CHI, ACM-SIGGraph, etc. Journals: IEEE Trans. visualization and computer graphics, etc. Data Mining: Concepts and Techniques 63

Recommended Reference Books n S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Recommended Reference Books n S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002 n R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2 ed. , Wiley-Interscience, 2000 n T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003 n U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996 n U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001 n J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2 nd ed. , 2006 n D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001 n T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001 n T. M. Mitchell, Machine Learning, Mc. Graw Hill, 1997 n G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991 n P. -N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 n S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998 n I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2 nd ed. 2005 19 March 2018 Data Mining: Concepts and Techniques 64

19 March 2018 Data Mining: Concepts and Techniques 65 19 March 2018 Data Mining: Concepts and Techniques 65