amt_introduction to data mining
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AMT_Introduction to Data MiningTRANSCRIPT
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Introduction to Data Introduction to Data MiningMining
Ankur Teredesai,Assistant Professor,
Dept. of Computer Science, RIT.
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Data Mining: A KDD ProcessData Mining: A KDD Process
Data mining—core of knowledge discovery process
Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation
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Steps of a KDD Steps of a KDD ProcessProcess
Learning the application domain relevant prior knowledge and goals of application
Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation
Find useful features, dimensionality/variable reduction, invariant representation.
Choosing functions of data mining summarization, classification, regression, association,
clustering. Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation
visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge
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Necessity Is the Mother of Necessity Is the Mother of InventionInvention
Data explosion problem Automated data collection tools and mature database
technology lead to tremendous amounts of data
accumulated and/or to be analyzed in databases, data
warehouses, and other information repositories
We are drowning in data, but starving for knowledge!
Solution: Data warehousing and data mining
Data warehousing and on-line analytical processing
Mining interesting knowledge (rules, regularities,
patterns, constraints) from data in large databases
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What Is Data Mining?What Is Data Mining? Data mining (knowledge discovery from
data) 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.
Watch out: Is everything “data mining”? (Deductive) query processing. Expert systems or small ML/statistical programs
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Course OverviewCourse Overview Motivation: Why data mining?
What is data mining?
Data Mining: On what kind of data?
Data mining functionality
Are all the patterns interesting?
Classification of data mining systems
Major issues in data mining
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AssumptionsAssumptions
Database: The truth persists. A ‘good’ snapshot available. Structured or semi-structured.
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DatabasesDatabases
Relational. Transactional. XML Data – web? Bioinformatics and Gene data. Image Data.
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Buzz WordsBuzz Words Data Warehousing. Aggregation / OLAP / ROLAP /
MOLAP. Prediction / Modeling /
Classification. Clustering / Segmentation. Scalability. Churn. VLDB / SIGMOD / SIGKDD
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Evolution of Database Evolution of Database TechnologyTechnology
1960s: Data collection, database creation, IMS and network DBMS
1970s: Relational data model, relational DBMS implementation
1980s: RDBMS, advanced data models (extended-relational, OO, deductive,
etc.) Application-oriented DBMS (spatial, scientific, engineering, etc.)
1990s: Data mining, data warehousing, multimedia databases, and Web
databases
2000s Stream data management and mining Data mining with a variety of applications Web technology and global information systems
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Some PatternsSome Patterns Association rules
98% of people who purchase diapers also buy beer
Classification People with age less than 25 and salary >
40k drive sports cars Similar time sequences
Stocks of companies A and B perform similarly
Outlier Detection Residential customers for telecom company
with businesses at home
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Why?—Potential Why?—Potential ApplicationsApplications
Market analysis and management
Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation
Risk analysis and management
Forecasting, customer retention, improved underwriting, quality control, competitive analysis
Fraud detection and detection of unusual patterns (outliers)
Other Applications Text mining (news
group, email, documents) and Web mining
Stream data mining DNA and bio-data
analysis
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Market Analysis and Market Analysis and ManagementManagement
Where does the data come from? Credit card transactions, loyalty cards, discount coupons, customer complaint
calls, plus (public) lifestyle studies Target marketing
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
Associations/co-relations between product sales, & prediction based on such association
Customer profiling What types of customers buy what products (clustering or classification)
Customer requirement analysis identifying the best products for different customers predict what factors will attract new customers
Provision of summary information multidimensional summary reports statistical summary information (data central tendency and variation)
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Corporate Analysis & Risk Corporate Analysis & Risk ManagementManagement
Finance planning and asset evaluation cash flow analysis and prediction contingent claim analysis to evaluate assets cross-sectional and time series analysis (financial-
ratio, trend analysis, etc.) Resource planning
summarize and compare the resources and spending
Competition monitor competitors and market directions group customers into classes and a class-based
pricing procedure set pricing strategy in a highly competitive market
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Fraud Detection & Mining Unusual Fraud Detection & Mining Unusual PatternsPatterns
Approaches: Clustering & model construction for frauds, outlier analysis
Applications: Health care, retail, credit card service, telecomm. Auto insurance: ring of collisions Money laundering: suspicious monetary transactions Medical insurance
Professional patients, ring of doctors, and ring of references Unnecessary or correlated screening tests
Telecommunications: phone-call fraud Phone call model: destination of the call, duration, time of day or
week. Analyze patterns that deviate from an expected norm Retail industry
Analysts estimate that 38% of retail shrink is due to dishonest employees
Anti-terrorism
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Other ApplicationsOther Applications
Sports IBM Advanced Scout analyzed NBA game statistics (shots
blocked, assists, and fouls) to gain competitive advantage for New York Knicks and Miami Heat
Astronomy JPL and the Palomar Observatory discovered 22 quasars
with the help of data mining
Internet Web Surf-Aid IBM Surf-Aid applies data mining algorithms to Web access
logs for market-related pages to discover customer preference and behavior pages, analyzing effectiveness of Web marketing, improving Web site organization, etc.
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Data Mining and Business Data Mining and Business IntelligenceIntelligence
Increasing potentialto supportbusiness decisions End User
Business Analyst
DataAnalyst
DBA
MakingDecisions
Data Presentation
Visualization Techniques
Data MiningInformation Discovery
Data Exploration
OLAP, MDA
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
Data SourcesPaper, Files, Information Providers, Database Systems, OLTP
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Architecture: Typical Data Architecture: Typical Data Mining SystemMining System
Data Warehouse
Data cleaning & data integration Filtering
Databases
Database or data warehouse server
Data mining engine
Pattern evaluation
Graphical user interface
Knowledge-base
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Data Mining: On What Kinds of Data Mining: On What Kinds of Data?Data?
Relational database Data warehouse Transactional database Advanced database and information
repository Object-relational database Spatial and temporal data Time-series data Stream data Multimedia database Heterogeneous and legacy database Text databases & WWW
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Data Mining Data Mining FunctionalitiesFunctionalities
Concept description: Characterization and discrimination
Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions
Association (correlation and causality) Diaper Beer [0.5%, 75%]
Classification and Prediction
Construct 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
Presentation: decision-tree, classification rule, neural network
Predict some unknown or missing numerical values
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Data Mining Data Mining Functionalities (2)Functionalities (2)
Cluster analysis Class label is unknown: Group data to form new classes,
e.g., cluster houses to find distribution patterns Maximizing intra-class similarity & minimizing interclass
similarity Outlier analysis
Outlier: a data object that does not comply with the general behavior of the data
Noise or exception? No! useful in fraud detection, rare events analysis
Trend and evolution analysis Trend and deviation: regression analysis Sequential pattern mining, periodicity analysis Similarity-based analysis
Other pattern-directed or statistical analyses
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Are All the “Discovered” Patterns Are All the “Discovered” Patterns Interesting?Interesting?
Data mining may generate thousands of patterns: Not all of
them are interesting Suggested approach: Human-centered, query-based, focused
mining
Interestingness measures 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
Objective vs. subjective interestingness measures Objective: based on statistics and structures of patterns, e.g.,
support, confidence, etc.
Subjective: based on user’s belief in the data, e.g.,
unexpectedness, novelty, actionability, etc.
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Can We Find All and Only Interesting Can We Find All and Only Interesting Patterns?Patterns?
Find all the interesting patterns: Completeness
Can a data mining system find all the interesting patterns?
Heuristic vs. exhaustive search
Association vs. classification vs. clustering
Search for only interesting patterns: An optimization problem
Can a data mining system find only the interesting
patterns?
Approaches First general all the patterns and then filter out the
uninteresting ones.
Generate only the interesting patterns—mining query
optimization
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Data Mining: Confluence of Multiple Data Mining: Confluence of Multiple DisciplinesDisciplines
Data Mining
Database Systems
Statistics
OtherDisciplines
Algorithm
MachineLearning Visualization
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Data Mining: Classification Data Mining: Classification SchemesSchemes
General functionality Descriptive data mining
Predictive data mining
Different views, different classifications Kinds of data to be mined
Kinds of knowledge to be discovered
Kinds of techniques utilized
Kinds of applications adapted
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Multi-Dimensional View of Data Multi-Dimensional View of Data MiningMining
Data to be mined Relational, data warehouse, transactional, stream, object-
oriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW
Knowledge to be mined Characterization, discrimination, association, classification,
clustering, trend/deviation, outlier analysis, etc. Multiple/integrated functions and mining at multiple levels
Techniques utilized Database-oriented, data warehouse (OLAP), machine
learning, statistics, visualization, etc. Applications adapted
Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, Web mining, etc.
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OLAP Mining: Integration of Data Mining and OLAP Mining: Integration of Data Mining and Data WarehousingData Warehousing
Data mining systems, DBMS, Data warehouse
systems coupling No coupling, loose-coupling, semi-tight-coupling, tight-
coupling
On-line analytical mining data integration of mining and OLAP technologies
Interactive mining multi-level knowledge Necessity of mining knowledge and patterns at different levels
of abstraction by drilling/rolling, pivoting, slicing/dicing, etc.
Integration of multiple mining functions Characterized classification, first clustering and then
association
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Let’s gamble!Let’s gamble!
Find relevant patterns. Find information nuggets. Find it within a given amount of
time. Find it with given computing
resources.
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Burning IssuesBurning Issues
Web Mining. Scalability. Concept Change / Temporal
Adaptation Data Cleaning XML Databases.
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Hot topics in Data MiningHot topics in Data Mining
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Time OutTime Out
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Major Issues in Data Major Issues in Data MiningMining
Mining methodology Mining different kinds of knowledge from diverse data types,
e.g., bio, stream, Web Performance: efficiency, effectiveness, and scalability Pattern evaluation: the interestingness problem Incorporation of background knowledge Handling noise and incomplete data Parallel, distributed and incremental mining methods Integration of the discovered knowledge with existing one:
knowledge fusion User interaction
Data mining query languages and ad-hoc mining Expression and visualization of data mining results Interactive mining of knowledge at multiple levels of
abstraction Applications and social impacts
Domain-specific data mining & invisible data mining Protection of data security, integrity, and privacy
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SummarySummary
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.
Data mining systems and architectures Major issues in data mining
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A Brief History of Data Mining A Brief History of Data Mining SocietySociety
1989 IJCAI Workshop on Knowledge Discovery in Databases
(Piatetsky-Shapiro) 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)
1998 ACM SIGKDD, SIGKDD’1999-2001 conferences, and SIGKDD
Explorations
More conferences on data mining PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.
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Where to Find References?Where to Find References?
Data mining and KDD (SIGKDD: CDROM) Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery, KDD Explorations
Database systems (SIGMOD: CD ROM) Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT,
DASFAA Journals: ACM-TODS, IEEE-TKDE, JIIS, J. ACM, etc.
AI & Machine Learning Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), etc. Journals: Machine Learning, Artificial Intelligence, etc.
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.
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Recommended Reference Recommended Reference BooksBooks
R. Agrawal, J. Han, and H. Mannila, Readings in Data Mining: A Database Perspective, Morgan
Kaufmann (in preparation)
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge
Discovery and Data Mining. AAAI/MIT Press, 1996
U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and
Knowledge Discovery, Morgan Kaufmann, 2001
J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001
D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining,
Inference, and Prediction, Springer-Verlag, 2001
T. M. Mitchell, Machine Learning, McGraw Hill, 1997
G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press,
1991
S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998
I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques
with Java Implementations, Morgan Kaufmann, 2001
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What is DM : CS 590/CS What is DM : CS 590/CS 749749
Course Description: This course will provide students with
an opportunity to study the principles, algorithms, implementations and applications of data mining.
Introduction to data mining. Motivation from pattern recognition. Emergent applications of data mining.
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TopicsTopics
Techniques : Frequent pattern
mining Association rules Classification Clustering Latent Semantic
Indexing SVM/GP/Neural
Nets and scalability issues
Applications: 1. mining relational
datasets2. mining the web
(XML-datasets)3. mining data streams
(Recommendations) 4. Scalable data
mining.5. Spam Filtering
(maybe).
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Prerequisites : Database
Concepts 4003-485 / 4005-771.
Artificial Intelligence 4003-455 / 4005-750.
Probability and Statistics.
Or permission of the instructor.
This course is related to:
4003-485 Database Concepts
All courses in the AI Group.
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DetailsDetails Form Groups Decide Topics Decide Projects Final Exams (take home or in-
class) Course web-page :
www.cs.rit.edu/~amt/datamining/DataMiningCourseHome.htm
Newsgroup will be announced. Office hours will be decided.