Kein Folientitel

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Special Topics in Database Systems
Martin Ester Simon Fraser University School of
Computing Science CMPT 884 Spring 2009
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Introduction
Fayyad, Piatetsky-Shapiro Smyth 96

• Knowledge discovery in databases (KDD) is the
  process of (semi-)automatic extraction of
  knowledge from databases which is
• valid
• previously unknown
• and potentially useful.
• Remarks
• (semi)-automatic distinction from manual
  analysis / OLAP. Typically, some user
  interaction necessary.
• valid in the statistical sense.
• previously unknown not explicit, no common
  sense knowledge.
• potentially useful for some given application.

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Introduction

• Statistics Hand, Mannila Smyth 2001
• representation of uncertainty
• model-based inferences
• focus on numeric data
• Machine Learning Mitchell 1997
• knowledge representation
• search strategies
• focus on symbolic data
• Database Systems Han Kamber 2000
• data management
• integration of data mining with DBS
• scalability for large databases

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Introduction
KDD Process Han Kamber 2000
Databases
KDD Process Fayyad, Piatetsky-Shapiro Smyth
1996
Data Mining
Trans- formation
Pre-processing
Focussing
Evaluation
Pattern
Knowledge
Database
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Data Mining

• Definition Fayyad, Piatetsky-Shapiro, Smyth
  1996
• Data Mining is the application of efficient
  algorithms to determine the patterns contained in
  some database.
• Data-Mining Tasks

clustering
classification
A and B ? C
association rules
generalisation
other tasks regression, outlier detection . . .
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Trends in KDD Research

• KDD 2000 Conference
• New Data Mining Algorithms
• Efficiency and Scalability of Data Mining
  Algorithms
• Interactive Data Exploration
• Visualization
• Constraints and Evaluation in the KDD Process

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Trends in KDD Research

• KDD 2002 Conference
• Statistical Methods
• Frequent Patterns
• Streams and Time Series
• Visualization
• Web Search and Navigation
• Text and Web Page Classification
• Intrusion and Privacy
• Applications

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Trends in KDD Research

• KDD 2004 Conference
• Frequent Patterns / Association Rules
• Clustering
• Mining Spatio-Temporal Data
• Mining Data Streams
• Dimensionality Reduction
• Privacy-Preserving Data Mining
• Mining Biological Data
• Applications (Web, biological data, security, .
  . .)

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Trends in KDD Research

• KDD 2006 Conference
• Clustering
• Classification / supervised ML
• Privacy
• Web / Graph Mining
• Web / Text Mining
• Frequent Pattern Mining
• Structured Data

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Trends in KDD Research

• KDD 2008 Conference
• Text Mining
• Data Integration
• Social Networks
• Graph Mining
• Distance Functions and Metric Learning
• Active and Semi-supervised Learning
• Pattern Mining
• Collaborative Filtering

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Trends in KDD Research

• Some Hot Topics
• Social Networks THE hot topic of KDD 08 ?
  topic of the only panel
• Graph mining
• Text mining and information extraction /
  integration
• Collaborative Filtering more general,
  recommender systems ? 1M NetFlix prize

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Overview of this Course

• Prerequisites
• Foundations of database systems and statistics
• Introductory graduate data mining course or
  equivalent
• Objectives
• Introduction into some hot topics of data mining
  research
• Training in research methodology
• Presentation skills
• start thesis work after this class!

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Overview of this Course

• Topics
• Graph mining social network analysis and
  analysis of biological networks as driving
  applications
• Recommender systems in particular trust-based
  recommendation
• Information extraction and integration
  integration with existing databases

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Overview of this Course

• Format
• Tutorial surveys by instructor
• Written research paper reviews by students
• Research paper presentations by
  students discussions in class
• Course research projects by students on a
  topic of their choice

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Overview of this Course

• Tentative Grading Scheme
• Paper review (20 )
• Paper presentation (20 )
• Course project report (40) two steps
  project proposal, final project report
• Course project presentation (20 )
• ? marking criteria originality, technical
  quality, presentation

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Overview of this Course

• Types of Course Projects
• Literature survey summarize the
  state-of-the-art and identify open research
  problems
• New problem introduce and analyze a new problem
• New algorithm for known problem implement and
  evaluate algorithm
• Improvement of existing algorithm implement and
  compare algorithm
• Comparison of existing algorithms on a new,
  interesting dataset identify criteria for choice
  of algorithms / open research problems

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Graph Mining

• Motivating Applications
• Social network analysis
• What communities exist?
• How does information about a new product spread?
• What customers should be targeted to maximize the
  profit of a marketing campaign?
• Analysis of biological networks
• o What are the functional modules of an organism?
• o How do biological networks evolve in the course
  of time?
• o What protein should be targeted to inhibit some
  virulent bacteria?

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Graph Mining

• Methods
• Frequent subgraph mining
• frequent pattern mining approach
• Graph clustering e.g., normalized cut, i.e.
  Minimize number of edges between graph
  components / clusters
• Graph generative models probabilistic models
  that generate graphs similar to real graphs /
  networks

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Graph Mining

• Challenges
• Complexity of graph algorithms
• Many graph mining problems are NP-hard.
• Real graphs tend to be extremely large.
• ? need efficient algorithms
• Attribute data
• Many graphs have attributes associated with the
  nodes.
• Transformation into weighted graph looses a lot
  of information.
• ? need new models / algorithms considering
  relationship and attribute data

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Recommender Systems

• Motivating Applications
• Motivation o The internet provides a flood of
  information on all kinds of items. o There is a
  great need for personalized recommendations.
  o The internet also provides a wealth of item
  ratings / reviews.
• Typical applications
• Movie recommendation
• Product recommendation
• Keyword recommendation

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Recommender Systems

• Methods
• Collaborative filtering o Uses only a database
  of user item ratings. o Recommendation based
  on ratings by users with similar rating patterns.
• Content-based recommender systems
• o Uses information about the content of items
  and / or the properties of users.
• o Recommends items that have content similar to
  items liked by user.
• Trust-based recommender systems
• Assume a social network / trust network. Trust
  can be defined explicitly or implicitly.
• Recommendation based on ratings by trusted
  neighbors.

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Recommender Systems

• Challenges
• High dimensionality and sparsity of data o The
  overwhelming majority (gt 99) of user item
  ratings is unknown. o Recommendation especially
  hard for cold start users and controversial
  items.
• ? dimensionality reduction, model based methods,
  trust-based approach
• Fraud
• o Memory-based collaborative filtering can be
  easily manipulated by adding fraudulent
  ratings.
• ? trust-based approach more robust to fraud
• Privacy issues with trust network data
• o only very few trust networks are public domain

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Information Extraction and Integration

• Motivating Applications
• Importance of unstructured text data o The
  overwhelming majority (gt 80) of human generated
  information is not in structured form,
  but in unstructured text.
• Biomedical literature
• o Contains a wealth of valuable information that
  cannot be processed / searched
  automatically.
• o Extraction of entities and relationships such
  as proteins and their localizations.
• Online product reviews
• o A lot of product reviews available online in
  community databases or blogs.
• o Companies want to know what customers think of
  their products.

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Information Extraction and Integration

• Methods
• Basic NLP methods o Part-of-speech tagging
• o Lexica, ontologies, . . .
• Machine learning methods
• o Typically, supervised classification.
• o CRFs and similar methods are state-of-the-art.
• Bootstrapping approach
• o Using a small labeled training dataset, find
  textual extraction patterns.
• o Using these patterns, extract further entities
  / relationships and continue.

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Information Extraction and Integration

• Challenges
• Text data is hard to understand o Many of the
  NLP problems are still essentially unsolved. ?
  relatively simple NLP methods often sufficient
  for information extraction
• Portability across domains
• o Extraction methods need to be portable from
  one domain to another.
• o Knowledge engineering approach (domain expert
  defines rules) is labor-intensive and
  expensive.
• ? machine learning methods
• Entity mentions need to be resolved
• o Information extraction produces strings
  referencing an entity of a given type.
• o Without mapping to known real world entities,
  extracted information is of limited
  usefulness. ? need to integrate extracted
  information with existing databases

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References

• Graph mining
• X Yan Karsten Borgwardt, "Graph Mining and
  Graph Kernels", Tutorial KDD 08
• Jure Leskovec and Christos Faloutsos, Mining
  Large Graphs Models, Diffusion and Case
  Studies, Tutorial ECML/PKDD 2007
• Recommender systems
• Joseph Konstan, Introduction to Recommender
  Systems, Tutorial SIGMOD 2008
• Information extraction and integration - Eugene
  Agichtein Sunita Sarawagi, Scalable
  Information Extraction and Integration,
  Tutorial KDD 06
• - AnHai Doan Raghu Ramakrishnan Shiv
  Vaithyanathan, Managing Information
  Extraction, Tutorial SIGMOD 2006