Introduction to DataMining

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Introduction toData-Mining

• Marko Grobelnik
• Institut Jozef Stefan

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Outline

• Motivation Definition
• What are typical applications?
• How do we build solutions?
• Method algorithms
• Tools standards
• conclusion

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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!

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Data pyramid
Wisdom
Knowledge experience
Knowledge
Information rules
Information
Data context
Data
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What Is Data Mining?

• Data mining (knowledge discovery in databases -
  KDD, business intelligence)
• Extraction of interesting ( non-trivial,
  implicit, previously unknown and potentially
  useful) information from data in large databases
• Tell me something interesting about the data.
• Describe the data.

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Potential Applications

• Database analysis and decision support
• Market analysis and management
• Risk analysis and management
• Fraud detection and management
• Text analysis - Text Mining
• Web analysis - Web Mining
• Intelligent query answering

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Market Analysis and Management

• Where are the data sources for analysis?
• 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
• Conversion of single to a joint bank account
  marriage, etc.

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Analysis and Risk Management

• Finance planning and asset evaluation
• cash flow analysis and prediction
• time series analysis (trend analysis, etc.)
• Resource planning
• summarize and compare the resources and spending
• Competition
• Monitor competitors and market directions
• Set pricing strategy in a highly competitive
  market

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Fraud Detection and Management

• Use historical data to build models of fraudulent
  behavior and use data mining to help identify
  similar instances
• Examples application
• Auto Insurance detect a group of people who
  stage accidents to collect on insurance
• Money Laundering detect suspicious money
  transactions
• Detecting telephone fraud detecting suspicious
  patterns (generate call model - destination,
  time, duration)

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Other Areas of application

• Sports
• Analysis of game in NBA (eg., detect the
  opponents strategy)
• Astronomy
• discovery and classification of new objects
• Internet
• analysis of Web access logs, discovery of user
  behavior patterns, analyzing effectiveness of Web
  marketing, improving Web site organization
• Text
• news analysis, medical record analysis, automatic
  email sorting and filtering, automatic document
  categorization

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Data mining intersection of multiple
disciplines

• Database systems, data warehouse and OLAP
• Statistics
• Machine learning
• Visualization
• Information science
• High performance computing
• Other disciplines
• Neural networks, mathematical modeling,
  information retrieval, pattern recognition, ...

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From data to knowledge
Knowledge

• Data mining the core of knowledge discovery
  process.

Pattern Evaluation
Data Mining
Task-relevant Data
Selection
Data Warehouse
Data Cleaning
Data Integration
Databases
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Main steps of KDD

• Learning the application domain
• relevant prior knowledge and goals of application
• Data cleaning and preprocessing (may take 60 of
  effort!)
• creating a target data set data selection
• find useful features, generate new features, map
  feature values, discretization of values
• Choosing data mining tools/algorithms
• summarization, classification, regression,
  association, clustering.
• Data mining search for patterns of interest
• Interpretation analysis of results.
• visualization, transformation, removing redundant
  patterns, etc.
• Use of discovered knowledge.

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Data Mining and Business Intelligence
Increasing potential to support business decisions
End User
Making Decisions
Business Analyst
Data Presentation
Visualization Techniques
Data Mining
Data Analyst
Information Discovery
Data Exploration
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
OLAP, MDA
DBA
Data Sources
Paper, Files, Information Providers, Database
Systems, OLTP
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Mining the data what kind of data?

• Relational databases
• Data warehouses
• Transactional databases
• Advanced DB systems and information repositories
  object-oriented and object-relational databases,
  spatial databases, time-series data and temporal
  data, text databases and multimedia databases,
  heterogeneous and legacy databases, WWW

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Data mining algorithms (I)

• Association
• finding rules like if the customer bought item
  A, then in X of transactions she/he also bought
  item B. This holds for Y of all transactions
• Classification and Prediction
• classify data based on the values in a
  classifying attribute, e.g., classify countries
  based on climate, or classify cars based on gas
  mileage
• predict some unknown or missing attribute values
  based on other information

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Data mining algorithms (II)

• Clustering
• group data to form new classes, e.g., find
  groups of customers with similar behavior
• Time-series analysis
• trend and deviation analysis find and
  characterize evolution trend, sequential
  patterns, similar sequences, and deviation data,
  e.g., stock analysis.
• similarity-based pattern-directed analysis find
  and characterize user-specified patterns in
  large databases.
• cyclicity/periodicity analysis find
  segment-wise or total cycles or periodic
  behaviors in time-related data.
• Other pattern-directed or statistical analysis

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Association rules

• Finding associations or correlations among a set
  of items
• Applications
• basket data analysis, cross-marketing,
• Example
• buying beer and chips -gt ketchup 0.5,60
• rule formLHS RHS support, confidence

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Classification

• Finding rules that describe given groups of
  objects
• Applications credit approval, target marketing,
  medical diagnosis, treatment effectiveness
  analysis,...
• Example based on the past symptoms and diagnoses
  of patients generate a model describing influence
  of symptoms to disease to be used for
  classification of future test data and better
  understanding of each class
• Methods decision-trees (e.g., ID3, C5),
  statistics, neural networks,...

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Classification using decision trees

• A decision tree
• Top-down decision tree generation algorithm, at
  each step
• partition examples based on the selected
  attribute value
• select attribute favoring the partitioning which
  makes the majority of examples belong to a single
  class

outlook
sunny
rain
overcast
windy
humidity
P
N
P
N
P
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Classification methods

• Decision trees and decision rules
• give a training set of labeled data
• tree pruning used for noise handling and avoiding
  data overfiting
• Bayesian classification
• Naïve Bayesian classification
• Bayesian belief networks
• Neural network approach
• multi-layer networks and back-propagation
• Genetic algorithms
• genetic operators (mutation, cross-over,) and
  fitness function selection

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Clustering methods

• partitioning a set of data into a set of classes,
  called clusters, such that the members of each
  class are sharing some interesting common
  properties.
• high quality clusters if the intra-class
  similarity is high and the inter-class similarity
  is low
• Important is distance measure

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Data-Mining tools

• Main producers of Data-Mining software
• IBM Intelligent Miner, extender for DB2
• SAS Enterprise Miner
• SPSS Clementine
• Microsoft Analysis Server (part of SQL Server
  2000)
• many more smaller producers

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Data Mining standards

• PMML (Predictive Modelling Markup Language)
• XML like language for saving and sharing models
  (most widely accepted standard)
• CRISP
• standardized methodology for building Data Mining
  applications
• OLE DB for Data Mining
• Microsofts standard for developing OLEDB/COM
  components for extending Analysis server with new
  Data Mining functionality (uses customized SQL
  language)
• IBM and Oracle prepared standard extensions to
  SQL language to support Data Mining functionality

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conclusion

• Data Mining is an area in the rapid development
• Who and Why needs Data Mining?
• (almost) everybody having the data?
• to get something more out of the data
• More information
• http//www.kdnuggets.com/