Data Mining Algorithms - Quretec

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jointly with Jiawei Han a Data Mining Summer Course in Shanghai, China in. July 1998. He has ... slides used for a Data Mining course at Univ. of Munich jointly taught with. Prof. Dr. Martin Ester (now teaching ..... M. H. Dunham. Data Mining: ...
Data Management and Exploration Prof. Dr. Thomas Seidl

Data Mining Algorithms Lecture Course with Tutorials Wintersemester 2003/04 RWTH Aachen, Lehrstuhl für Informatik IX – Data Management and Exploration – Prof. Dr. Thomas Seidl

Data Management and Exploration Prof. Dr. Thomas Seidl

Course Organization (1) „

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Weekly Schedule „

Tuesday, 13:45 – 15:15 h, AH VI (Lecture)

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Thursday, 13:45 – 15:15 h, AH VI (Lecture)

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Friday, 10:00 – 11:30 h, AH VI (Tutorials) Begins: Oct. 24th

People „

Prof. Dr. Thomas Seidl Office hour: Monday, 13:00 – 14:00 h

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Dipl.-Inform. Ira Assent

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Dipl.-Inform. Christoph Brochhaus

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Course Organization (2) „

Credits „ „

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Lecture course with tutorials (4 + 2 hrs/wk) 8 ECTS for “Praktische Informatik” or specialization “Datenbanken und Datenexploration” / “Databases and Data Mining”

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Selected exercises as homework (50% points for exam admission)

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Oral or written exam at the end of semester

Material (Slides, Exercises, etc.) available from: http://www-i9.informatik.rwth-aachen.de/Lehre/ DataMiningAlgorithmen/

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Content of the Course „

Introduction

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Data warehousing and OLAP for data mining

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Data preprocessing

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Clustering

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Classification and prediction techniques

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Mining association rules in large databases

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Further topics

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Textbook / Acknowledgements The slides used in this course are modified versions of the copyrighted original slides provided by the authors of the adopted textbooks: © Jiawei Han and Micheline Kamber: Data Mining – Concepts and Techniques Morgan Kaufmann Publishers, 2000. http://www.cs.sfu.ca/~han/dmbook © Martin Ester and Jörg Sander: Knowledge Discovery in Databases – Techniken und Anwendungen Springer Verlag, 2000 (in German). WS 2003/04

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Source and History of the Slides „

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Prof. Dr. Jiawei Han started working on this set of slides for an UCLA Extension course in February 1998. Dr. Hongjun Lu from Hong Kong Univ. of Science and Technology taught jointly with Jiawei Han a Data Mining Summer Course in Shanghai, China in July 1998. He has contributed many slides to it. Some graduate students have contributed many new slides in the following years. Notable contributors include Eugene Belchev, Jian Pei, and Osmar R. Zaiane (now teaching in Univ. of Alberta). Prof. Dr. Jörg Sander, Univ. of Alberta in Edmonton, Canada provided some more slides, particularly on clustering algorithms. They are an extension of slides used for a Data Mining course at Univ. of Munich jointly taught with Prof. Dr. Martin Ester (now teaching in Simon-Fraser-Univ. in Vancouver).

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Chapter 1. Introduction „

Motivation: Why data mining?

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What is data mining?

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Data Mining: On what kind of data?

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Data mining functionality

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Are all the patterns interesting?

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Classification of data mining systems

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Major issues in data mining

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Motivation: “Necessity is the Mother of Invention” „

Data explosion problem „

Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories

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We are drowning in data, but starving for knowledge!

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Solution: Data warehousing and data mining „

Data Warehousing and on-line analytical processing (OLAP)

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Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases

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Evolution of Database Technology „

1960s: „

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1970s: „

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Relational data model, relational DBMS implementation

1980s: „

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Data collection, database creation, IMS and network DBMS

RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.)

1990s—2000s: „

Data mining and data warehousing, multimedia databases, and Web databases

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What Is Data Mining? „

Data mining (knowledge discovery in databases): „

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Alternative names and their “inside stories”: „ „

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Data mining: a misnomer? Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging (“Ausbaggern”), information harvesting, business intelligence, etc.

What is not data mining? „ „

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Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases

(Deductive) query processing. Expert systems or small ML/statistical programs Data Mining Algorithmen

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Why Data Mining? — Potential Applications

Data Management and Exploration Prof. Dr. Thomas Seidl

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Database analysis and decision support „

Market analysis and management „

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Risk analysis and management „

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target marketing, customer relation management, market basket analysis, cross selling, market segmentation Forecasting, customer retention (Eigenbeteiligung), improved underwriting, quality control, competitive analysis

Fraud detection and management

Other Applications „

Text mining (news group, email, documents) and Web analysis.

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Intelligent query answering

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Market Analysis and Management (1) „

Where are the data sources for analysis? „

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Target marketing „

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Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.

Determine customer purchasing patterns over time „

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Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies

Conversion of a single to a joint bank account: marriage, etc.

Cross-market analysis

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Associations/co-relations between product sales

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Prediction based on the association information Data Mining Algorithmen

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Market Analysis and Management (2) „

Customer profiling „

data mining can tell you what types of customers buy what products (clustering or classification)

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Identifying customer requirements „

identifying the best products for different customers

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use prediction to find what factors will attract new customers

Provide summary information „

various multidimensional summary reports

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statistical summary information (data central tendency and variation of the data)

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Corporate Analysis and Risk Management „

Finance planning and asset evaluation „ „ „

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Resource planning: „

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cash flow analysis and prediction contingent claim analysis to evaluate assets cross-sectional and time series analysis (financial-ratio, trend analysis, etc.) summarize and compare the resources and spending

Competition: „ „

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monitor competitors and market directions group customers into classes and a class-based pricing procedure set pricing strategy in a highly competitive market Data Mining Algorithmen

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Fraud Detection and Management „

Applications „

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Approach „

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widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc. use historical data to build models of fraudulent behavior and use data mining to help identify similar instances

Examples „

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auto insurance: detect a group of people who stage accidents to collect on insurance money laundering: detect suspicious money transactions (e.g., US Treasury's Financial Crimes Enforcement Network) medical insurance: detect professional patients and ring of doctors and ring of references

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Data Mining and Business Intelligence Increasing potential to support business decisions

Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery

End User

Business Analyst Data Analyst

Data Exploration Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts OLAP, MDA Data Sources Paper, Files, Information Providers, Database Systems, OLTP WS 2003/04

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DBA

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Architecture of Typical Data Mining Systems Graphical user interface

Pattern evaluation Data mining engine Database or data warehouse server Data cleaning & data integration

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Knowledge-base Filtering

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Data Mining: On What Kind of Data? „ „ „ „

Relational databases Data warehouses Transactional databases Advanced DB and information repositories „ „ „ „ „ „

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Object-oriented and object-relational databases Spatial databases Time-series data and temporal data Text databases and multimedia databases Heterogeneous and legacy databases WWW Data Mining Algorithmen

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The KDD Process

Selections Projections Transformations

Data Mining Task-relevant Data

Visualization Evaluation Patterns

Knowledge

“Data Warehouse” Data Cleaning Data Integration

Data Mining: • a central step in the process • application of algorithms that find “patterns” in the data.

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Steps of a KDD Process „

Learning the application domain: „

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Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation: „

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summarization, classification, regression, association, clustering.

Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation „

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Find useful features, dimensionality/variable reduction, invariant representation.

Choosing functions of data mining „

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relevant prior knowledge and goals of application

visualization, transformation, removing redundant patterns, etc.

Use of discovered knowledge

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Data Cleaning and Integration „

Integration of data from different sources „

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Mapping of attribute names Data Cleaning (e.g. C_Nr → O_Id) Data Integration “Data Warehouse” Joining different tables (e.g. Table1 = [C_Nr, Info1] Databases/ Information Repositories and Table2 = [O_Id, Info2] ⇒ JoinedTable = [O_Id, Info1, Info2])

Elimination of inconsistencies Elimination of noise Computation of Missing Values (if necessary and possible) „

Fill in missing values by some strategy (e.g. default value, average value, or application specific computations)

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Focusing on task-relevant data „

Selections „

Select the relevant tuples/rows from the database tables

Selections Projections Transformations

(e.g., sales data for the year 2001) „

Projections „

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Task-relevant Data

“Data Warehouse”

Select the relevant attributes/columns from the database tables (e.g., “id”, “date” “amount” from (Id, name, date, location, amount))

Transformations, e.g.: „

Normalization (e.g., age:[18, 87] Æ n_age:[0, 100]

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Discretisation of numerical attributes

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Computation of derived tuples/rows and derived attributes

(e.g., amount:[0, 100] Æ d_amount:{low, medium, high} „ „

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aggregation of sets of tuples, e.g., total amount per months new attributes, e.g., diff = sales current month – sales previous month) Data Mining Algorithmen

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Basic Data Mining Tasks Data

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Mining Clustering Task-relevant Patterns Data Classification Association Rules Concept Characterization and Discrimination Other methods „ „ „ „

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Outlier detection Sequential patterns Trends and analysis of changes Methods for special data types, e.g., spatial data mining, web mining …

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Clustering „

Class labels are unknown: Group objects into sub-groups (clusters) „

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Similarity function (or dissimilarity fct. = distance) to measure similarity between objects Objective: “maximize” intra-class similarity and “minimize” interclass similarity

Applications

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Customer profiling/segmentation Document or image collections Web access patterns ...

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Classification Class labels are known for a small set of “training data”: Find models/functions/rules (based on attribute values of the training examples) that

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describe and distinguish classes predict class membership for “new” objects

Applications

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a a

a

aa ba a

b b b ab b a b b b

Classify gene expression values for tissue samples to predict disease type and suggest best possible treatment Automatic assignment of categories to large sets of newly observed celestial objects Predict unknown or missing values (Æ KDD pre-processing step) ...

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Association Rules Find frequent associations in transaction databases

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Frequently co-occurring items in the set of transactions (frequent itemsets): indicate correlations or causalities „ Rules with a minimum confidence Transaction ID Items Bought based on frequent itemsets „

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Applications: „ „ „ „

2000

A,B,C

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A,C

Market-basket analysis 4000 Cross-marketing 5000 Catalog design Also used as a basis for clustering, classification

A,D B,E,F

Examples: buys(x, “diapers”) → buys(x, “beers”) [support: 0.5%, confidence: 60%] major(x, “CS”) ^ takes(x, “DB”) → grade(x, “A”) [support: 1%, confidence: 75%] WS 2003/04

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Generalization, Characterization, Discrimination „

Generalize, summarize, and contrast data characteristics „

Based on attribute aggregation along concept hierarchies „ Data cube approach (OLAP) „ Attribute-oriented induction approach all

all Europe

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country

city

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North_America

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Spain

Canada

Vancouver

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L. Chan

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Mexico

Toronto

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Other Methods „

Outlier detection „

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Trends and Evolution Analysis „ „

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Sequential patterns (find re-occurring sequences of events) Regression analysis

Methods for special data types, and applications e.g., „ „ „ „ „

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Find objects that do not comply with the general behavior of the data (fraud detection, rare events analysis)

Spatial data mining Web mining Bio-KDD Graphs ...



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Evaluation and Visualization „

Integration of visualization and data mining „ „ „ „

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data visualization data mining result visualization data mining process visualization interactive visual data mining

Visualization Evaluation Patterns

Knowledge

Different types of 2D/3D plots, charts and diagrams are used, e.g.: „ „ „ „ „

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Box-plots Trees X-Y-Plots Parallel coordinates ... 29

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Are All the “Discovered” Patterns Interesting? „

A data mining system/query may generate thousands of patterns, not all of them are interesting. „

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Interestingness measures: „

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Suggested approach: Human-centered, query-based, focused mining 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: „

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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. Data Mining Algorithmen

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Can We Find All and Only Interesting Patterns? „

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Find all the interesting patterns: Completeness „

Can a data mining system find all the interesting patterns?

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Association vs. classification vs. clustering

Search for only interesting patterns: Optimization „

Can a data mining system find only the interesting patterns?

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Approaches „

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First generate 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 Disciplines Database Technology

Machine Learning

Statistics

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Other Disciplines Data Mining Algorithmen

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Data Mining: Classification Schemes „

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General functionality „

Descriptive data mining

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Predictive data mining

Different views, different classifications „

Kinds of databases to be mined

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Kinds of knowledge to be discovered

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Kinds of techniques utilized

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Kinds of applications adapted 33

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A Multi-Dimensional View of Data Mining Classification „

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Databases to be mined „ Relational, transactional, object-oriented, object-relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW, etc. Knowledge to be mined „ Characterization, discrimination, association, classification, clustering, trend, deviation and outlier analysis, etc. „ Multiple/integrated functions and mining at multiple levels Techniques utilized „ Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, neural network, etc. Applications adapted „

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Retail, telecommunication, banking, fraud analysis, DNA mining, stock market analysis, Web mining, Weblog analysis, etc. Data Mining Algorithmen

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OLAP Mining: An Integration of Data Mining and Data Warehousing „

Data mining systems, DBMS, Data warehouse systems coupling „

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On-line analytical mining data „

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integration of mining and OLAP technologies

Interactive mining multi-level knowledge „

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No coupling, loose-coupling, semi-tight-coupling, tight-coupling

Necessity of mining knowledge and patterns at different levels of abstraction by drilling/rolling, pivoting, slicing/dicing, etc.

Integration of multiple mining functions „

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Characterized classification, first clustering and then association 35

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Major Issues in Data Mining (1) „

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Mining methodology and user interaction „

Mining different kinds of knowledge in databases

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Interactive mining of knowledge at multiple levels of abstraction

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Incorporation of background knowledge

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Data mining query languages and ad-hoc data mining

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Expression and visualization of data mining results

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Handling noise and incomplete data

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Pattern evaluation: the interestingness problem

Performance and scalability „

Efficiency and scalability of data mining algorithms

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Parallel, distributed and incremental mining methods

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Major Issues in Data Mining (2) Issues relating to the diversity of data types

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Handling relational and complex types of data Mining information from heterogeneous databases and global information systems (WWW)

Issues related to applications and social impacts

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Application of discovered knowledge „ Domain-specific data mining tools „ Intelligent query answering „ Process control and decision making Integration of the discovered knowledge with existing knowledge: A knowledge fusion problem Protection of data security, integrity, and privacy

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Summary „

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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.

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Classification of data mining systems

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Major issues in data mining

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A Brief History of Data Mining Society „

1989 IJCAI (Int‘l Joint Conf. on Artificial Intelligence) Workshop on Knowledge Discovery in Databases (Piatetsky-Shapiro) „

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Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. PiatetskyShapiro, P. Smyth, and R. Uthurusamy, 1996)

1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’95-98) „

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Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)

1991-1994 Workshops on Knowledge Discovery in Databases „

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Data Management and Exploration Prof. Dr. Thomas Seidl

Journal of Data Mining and Knowledge Discovery (1997)

1998 ACM SIGKDD, SIGKDD’1999-2001 conferences, and SIGKDD Explorations More conferences on data mining „

PAKDD, PKDD, SIAM-Data Mining, (IEEE) ICDM, etc.

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Where to Find References? „

Data mining and KDD (SIGKDD member CDROM): „ „

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Database field (SIGMOD member CD ROM): „

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Conference proceedings: Machine learning, AAAI, IJCAI, etc. Journals: Machine Learning, Artificial Intelligence, etc.

Statistics: „ „

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Conference proceedings: ACM-SIGMOD, ACM-PODS, VLDB, ICDE, EDBT, DASFAA Journals: ACM-TODS, J. ACM, IEEE-TKDE, JIIS, etc.

AI and Machine Learning: „

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Conference proceedings: KDD, and others, such as PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery

Conference proceedings: Joint Stat. Meeting, etc. Journals: Annals of statistics, etc.

Visualization: „ „

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Conference proceedings: CHI (Comp. Human Interaction), etc. Journals: IEEE Trans. visualization and computer graphics, etc. Data Mining Algorithmen

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Data Management and Exploration Prof. Dr. Thomas Seidl

References „

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U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining . AAAI/MIT Press, 1996. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000. T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of the ACM, 39:58-64, 1996. G. Piatetsky-Shapiro, U. Fayyad, and P. Smith. From data mining to knowledge discovery: An overview. In U.M. Fayyad, et al. (eds.), Advances in Knowledge Discovery and Data Mining, 1-35. AAAI/MIT Press, 1996. G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991. M. Ester and J. Sander. Knowledge Discovery in Databases: Techniken und Anwendungen. Springer Verlag, 2000 (in German). M. H. Dunham. Data Mining: Introductory and Advanced Topics. Prentice Hall, 2003. D. Hand, H. Mannila, P. Smyth. Principles of Data Mining. MIT Press, 2001.

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