Download Data Mining - ICAR

Why Intelligent Data
Analysis?
Joost N. Kok
Leiden Institute of Advanced
Computer Science
Universiteit Leiden
Overview
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Data Analysis
Data Mining
Applications
Outlook
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Data Analysis
Data Mining
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• ``Data Mining is one of the five
key note technologies that will
have a major impact across a
wide range of industries within
the next three to five years’’
(Gartner)
• ``Data Mining is one of the top
ten new technologies in which
companies will invest during
the next five years’’ (Gartner)
• ``Data Mining is an overhyped
concept’’ (OTR)
Data Analysis
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• Data analysis = Processing data
• Exploratory vs. Confirmatory
– are there interesting structures?
– can we predict the value?
• Descriptive vs. Inferential
– statement about data set
– draw more general conclusions
• Data analysis = process of
computing various summaries
and derived values from the
given collection of data
Tools
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• Cookbook fallacy:
Data analysis = picking and
applying the right tool.
– Tools are not independent.
– Matching is an iterative process
(which needs intelligence).
Stat vs. ML
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• Statistics
– Mathematics
• Machine Learning
– Experimental Computer Science
• ``Statistics is difficult’’
• ``Algorithms are not exact’’
Models
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• Models vs. Algorithms
• Empirical vs. Mechanistic
Models
• Understanding vs. Prediction
• Models vs. Patterns
• Overfitting
• Constraints
Algorithms
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• Enabling data analysis
• Too many: often no
foundations, no applications
• In practice only a restricted set
of algorithms is used
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The nature of Data
• Different kinds of data
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Numerical Data
Text
Images
Sound
• Raw data has
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missing values
distortions
misrecording
inadequate sampling
etc.
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The nature of data
• Data sets can be large
– horizontal
– vertical
• Curse of dimensionality
• Experiments
• Sampling
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The nature of data
• Too little
– Example: storm situations
• Too much
– Example: image segmentation
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Static vs. dynamic
Off-line vs. On-line
Infoglut
What is collected?
Overview
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Statistical methods and concepts
Bayesian methods
Time series
Rule induction
Neural networks
Fuzzy logic
Stochastic search methods
Applications
Overview
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 Why Intelligent Data Analysis
 Fundamental Concepts of
Statistics
 Intelligent Data Analysis: Issues
and Challenges
 Artificial Neural Networks
 Fuzzy Logic
 Industrial Applications of NeuroFuzzy Networks
 Statistical Methods for Data
Analysis
 Time Series Analysis
Overview
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 Chaos and Reality
 Bayesian Networks
 ANN Visualization Tools
 Rule Induction
 Evolutionary Systems
 Data Analysis in Real-World
Applications
Enrichment
• Data Fusion
– combine data sets
• Example:
– customer database
– survey information
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Data Mining
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• Database technology
• Data visualization
• Data warehouse vs Operational
database
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time-dependent
non-volatile
subject-oriented
integrated
• Target: decision making
Data Mining
Data Mining
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Selection
Cleaning
Enrichment
Coding
Data Mining
Reporting
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Cleaning
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Remove duplicates
Check domain consistency
Remove data
Project data
Combine data in one table
Coding
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Adress - Region
Date of birth - Age
Scaling of numerical data
Date - Number of months
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Data Mining
• SQL queries
• Clustering
• Pattern Recognition
ML
ES
KDD
DB
Visual
Statistics
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Nearest Neighbor
• Search k nearest points
Oil Search
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• Shell research
• South-East Asia
coring
measurements
kinds of stone
Applications
Outlook
Outlook
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• Positive
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Moore’s Law
New kinds of computers
Data collection
More data is more easy reachable
• Negative
– Collective memory gets lost
– Infoglut
• Data battle
Outlook
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• Merge of Machine Learning and
Statistics
• Algorithms
– Adaptive parameters
– Black Box data mining
• From suites to tailored tools
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Intelligent Data Analysis
• Intelligent Data Analysis
– User Interaction
– also uses tools from Machine
Learning
NetTalk
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Sound
Sound
Generator
generator
Speech-synthesis
Speech-synthesis
expertsystem
system
expert
INTELLI
NetTalk
Neural Network