Data Mining Algorithms - Quretec

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


Course Organization (1)

Weekly Schedule

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

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

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

Dipl.-Inform. Ira Assent

Dipl.-Inform. Christoph Brochhaus

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Data Mining Algorithmen

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Course Organization (2)

Credits

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

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

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

Data warehousing and OLAP for data mining

Data preprocessing

Clustering

Classification and prediction techniques

Mining association rules in large databases

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

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?

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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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

We are drowning in data, but starving for knowledge!

Solution: Data warehousing and data mining

Data Warehousing and on-line analytical processing (OLAP)

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

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Evolution of Database Technology

1960s:

1970s:

Relational data model, relational DBMS implementation

1980s:

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):

Alternative names and their “inside stories”:

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


Database analysis and decision support

Market analysis and management

Risk analysis and management

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.

Intelligent query answering

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Market Analysis and Management (1)

Where are the data sources for analysis?

Target marketing

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

Determine customer purchasing patterns over time

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

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)

Identifying customer requirements

identifying the best products for different customers

use prediction to find what factors will attract new customers

Provide summary information

various multidimensional summary reports

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

Resource planning:

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

Approach

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

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


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

Data Warehouse 17



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:

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

summarization, classification, regression, association, clustering.

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

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

Choosing functions of data mining

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

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

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]

Discretisation of numerical attributes

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

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

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)

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

Applications

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

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

Applications

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

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

Applications:

2000

A,B,C

1000

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

region

country

city

Germany

Frankfurt

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North_America

...

Spain

Canada

Vancouver

...

L. Chan

...

...

...

Mexico

Toronto

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Other Methods

Outlier detection

Trends and Evolution Analysis

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

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

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

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



Are All the “Discovered” Patterns Interesting?

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

Interestingness measures:

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?

Find all the interesting patterns: Completeness

Can a data mining system find all the interesting patterns?

Association vs. classification vs. clustering

Search for only interesting patterns: Optimization

Can a data mining system find only the interesting patterns?

Approaches

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

Data Mining

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Visualization

Other Disciplines Data Mining Algorithmen

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Data Mining: Classification Schemes

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General functionality

Descriptive data mining

Predictive data mining

Different views, different classifications

Kinds of databases to be mined

Kinds of knowledge to be discovered

Kinds of techniques utilized

Kinds of applications adapted 33



A Multi-Dimensional View of Data Mining Classification

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

On-line analytical mining data

integration of mining and OLAP technologies

Interactive mining multi-level knowledge

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



Major Issues in Data Mining (1)

Mining methodology and user interaction

Mining different kinds of knowledge in databases

Interactive mining of knowledge at multiple levels of abstraction

Incorporation of background knowledge

Data mining query languages and ad-hoc data mining

Expression and visualization of data mining results

Handling noise and incomplete data

Pattern evaluation: the interestingness problem

Performance and scalability

Efficiency and scalability of data mining algorithms

Parallel, distributed and incremental mining methods

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

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

Issues related to applications and social impacts

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

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.

Classification of data mining systems

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)

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)

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

1991-1994 Workshops on Knowledge Discovery in Databases


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):

Database field (SIGMOD member CD ROM):

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

Statistics:

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

AI and Machine Learning:

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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References

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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