Temple University CIS Dept' CIS616 Principles of Data Management

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Temple University CIS Dept.CIS616 Principles
of Data Management

• V. Megalooikonomou
• Introduction to Data Mining
• (based on notes by Jiawei Han and Micheline
  Kamber and on notes by Christos Faloutsos)

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General Overview - rel. model

• Relational model - SQL
• Functional Dependencies Normalization
• Physical Design Indexing
• Query processing/optimization
• Transaction processing
• Advanced topics
• Distributed Databases
• OO- and OR-DBMSs
• Data Mining

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Agenda

• 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

• Data rich but information poor!
• 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
• Solution Data Mining
• Extraction of interesting knowledge (rules,
  regularities, patterns, constraints) from data
  in large databases

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Evolution of Database Technology

• 1960s
• Data collection, database creation, IMS and
  network DBMS
• 1970s
• Relational data model, relational DBMS
  implementation
• 1980s
• RDBMS, advanced data models (extended-relational,
  OO, deductive, etc.) and application-oriented
  DBMS (spatial, scientific, engineering, etc.)
• 1990s2000s
• Data mining and data warehousing, multimedia
  databases, and Web databases

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What Is Data Mining?

• Data mining (knowledge discovery in databases)
  
• Extraction of interesting (non-trivial, implicit,
  previously unknown and potentially useful)
  information or patterns from data in large
  databases
• Alternative names
• Knowledge discovery(mining) in databases (KDD),
  knowledge extraction, data/pattern analysis, data
  archeology, information harvesting, business
  intelligence, etc.
• What is not data mining?
• (Deductive) query processing.
• Expert systems or small ML/statistical programs

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What Is Data Mining?

• Now that we have gathered so much data,

what do we do with it?

• Extract interesting patterns (automatically)

• Associations (e.g., butter bread --gt milk)
• Sequences (e.g., temporal data related to stock
  market)
• Rules that partition the data (e.g., store
  location problem)

• What patterns are interesting?

information content, confidence and support,
unexpectedness, actionability (utility in
decision making))
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Why Data Mining? Potential Applications

• Database analysis and decision support
• Market analysis and management
• target marketing, market basket analysis,
• Risk analysis and management
• Forecasting, quality control, competitive
  analysis,
• Fraud detection and management
• Other Applications
• Text mining (newsgroup, email, documents) and Web
  analysis.
• Spatial data mining
• Intelligent query answering

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Market Analysis and Management

• Data sources for analysis? (Credit card
  transactions, discount coupons, customer
  complaint calls, etc.)
• Target marketing (Find clusters of model
  customers who share same characteristics
  interest, income level, spending habits, etc.)
• Customer purchasing patterns over time
  (Conversion of single to a joint bank account
  marriage, etc.)
• Cross-market analysis (Associations between
  product sales and prediction based on
  associations)
• Customer Profiling (What customers buy what
  products)
• Customer Requirements (Best products for
  different customers)
• Summary information (multidimensional summary
  reports)

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Risk Analysis and Management

• Finance planning and asset evaluation
• cash flow analysis and prediction
• cross-sectional and time series analysis
  (financial-ratio, trend analysis, etc.)
• Resource planning
• summarize and compare the resources and spending
• Competition
• monitor competitors and market directions
• group customers into classes and a class-based
  pricing procedure
• set pricing strategy in a highly competitive
  market

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Fraud Detection and Management

• Applications
• health care, retail, credit card services,
  telecommunications etc.
• Approach
• use historical data to build models of normal and
  fraudulent behavior and use data mining to help
  identify fraudulent instances
• Examples
• auto insurance detect groups who stage accidents
  to collect insurance
• money laundering detect suspicious money
  transactions
• medical insurance detect professional patients
  and ring of doctors, inappropriate medical
  treatment
• detecting telephone fraudTelephone call model
  destination of the call, duration, time of
  day/week. Analyze patterns that deviate from
  expected norm.

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Discovery of Medical/Biological Knowledge

• Discovery of structure-function associations
• Structure of proteins and their function
• Human Brain Mapping (lesion-deficit,
  task-activation associations)
• Cell structure (cytoskeleton) and functionality
  or pathology
• Discovery of causal relationships
• Symptoms and medical conditions
• DNA sequence analysis
• Bioinformatics (microarrays, etc)

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Other Applications

• Sports
• IBM Advanced Scout analyzed NBA game statistics
  (shots blocked, assists, and fouls) to gain
  competitive advantage for New York Knicks and
  Miami Heat
• Astronomy
• JPL and the Palomar Observatory discovered 22
  quasars with the help of data mining
• Internet Web Surf-Aid
• IBM Surf-Aid applies data mining algorithms to
  Web access logs for market-related pages to
  discover customer preference and behavior pages,
  analyzing effectiveness of Web marketing,
  improving Web site organization, etc.

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Data Mining A KDD Process
Knowledge
Pattern Evaluation

• Data mining the core of knowledge discovery
  process.

Data Mining
Task-relevant Data
Selection
Data Warehouse
Data Cleaning
Data Integration
Databases
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Steps of a KDD Process

• Learning the application domain
• relevant prior knowledge and goals of application
• Creating a target data set data selection
• Data cleaning and preprocessing (may take 60 of
  effort!)
• Data reduction and transformation
• Find useful features, dimensionality/variable
  reduction, invariant representation.
• Choosing functions of data mining
• summarization, classification, regression,
  association, clustering.
• Choosing the mining algorithm(s)
• Data mining search for patterns of interest
• Pattern evaluation and knowledge presentation
• visualization, transformation, removing redundant
  patterns, etc.
• Use of discovered knowledge

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Data Mining and Business Intelligence
End User
Making Decisions
Increasing potential to support business 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
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Architecture of a Typical Data Mining System
Graphical user interface
Pattern evaluation
Data mining engine
Knowledge-base
Database or data warehouse server
Data cleaning data integration
Filtering
Data Warehouse
Databases
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Data Mining On What Kind of Data?

• Relational databases
• Data warehouses
• Transactional databases
• Advanced DB and information repositories
• Object-oriented (OO)and object-relational (OR)
  databases
• Spatial databases (medical, satellite image DBs,
  GIS)
• Temporal databases
• Text databases
• Multimedia databases (Image, Video, etc)
• Heterogeneous and legacy databases
• WWW

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Data Mining Functionalities Patterns that can
be mined

• Concept description Characterization and
  discrimination
• Generalize, summarize, and contrast data
  characteristics, e.g., dry vs. wet regions
• Association (correlation and causality)
• Multi-dimensional vs. single-dimensional
  association
• age(X, 20..29) income(X, 20..29K) à buys(X,
  PC) support 2, confidence 60
• contains(T, computer) à contains(x, software)
  1, 75
• Confidence(x à y) P(yx) degree of certainty
  of association
• Support(x à y) P(x ?y) of transactions that
  the rule satisfies

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Data Mining Functionalities Patterns that can
be mined

• Classification and Prediction
• Finding models (e.g., if-then rules, decision
  trees, mathematical formulae, neural networks,
  classification rules) that describe and
  distinguish classes or concepts for future
  prediction, e.g., classify cars based on
  gasmileage
• Prediction Predict some unknown or missing
  numerical values
• Cluster analysis
• Class label is unknown Group data to form new
  classes, e.g., cluster houses to find
  distribution patterns
• Clustering principle maximize intra-class
  similarity and minimize interclass similarity

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Data Mining Functionalities Patterns that can
be mined

• Outlier analysis
• Outliers data objects that do not comply with
  the general behavior of the data (can be detected
  using statistical tests that assume a prob.
  model)
• Often considered as noise but useful in fraud
  detection, rare events analysis
• Trend and evolution analysis
• Study regularities of objects whose behavior
  changes over time
• Trend and deviation regression analysis
• Sequential pattern mining, periodicity analysis
• Similarity-based analysis

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When is a Discovered Pattern Interesting?

• A data mining system/query may generate thousands
  of patterns, not all of them are interesting.
• Suggested approach Human-centered, query-based,
  focused mining
• Interestingness measures 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
• Objective based on statistics and structures of
  patterns, e.g., support, confidence, etc.
• Subjective based on users belief in the data,
  e.g., unexpectedness, novelty, actionability, etc.

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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 patternsmining
  query optimization

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Data Mining Confluence of Multiple Disciplines
Database Technology
Statistics
Data Mining
Machine Learning
Visualization
Information Science
Other Disciplines
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Data Mining Classification Schemes

• 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

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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
• Retail, telecommunication, banking, fraud
  analysis, DNA mining, stock market analysis, Web
  mining, Weblog analysis, etc.

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Major Issues in Data Mining

• 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 to guide
  the discovery process
• 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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More details Decision Trees

• Decision trees
• Problem
• Approach
• Classification through trees
• Building phase - splitting policies
• Pruning phase (to avoid over-fitting)

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Decision Trees

• Problem Classification i.e., given a training
  set (N tuples, with M attributes, plus a label
  attribute) find rules, to predict the label for
  newcomers
• Pictorially

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Decision trees
??
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Decision trees

• Issues
• missing values
• noise
• rare events

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Decision trees

• types of attributes
• numerical ( continuous) - eg salary
• ordinal ( integer) - eg. of children
• nominal ( categorical) - eg. car-type

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Decision trees

• Pictorially, we have

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Decision trees

• and we want to label ?

?
num. attr2 (e.g., chol-level)
-
-



-

-

-

-

num. attr1 (e.g., age)
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Decision trees

• so we build a decision tree

?
num. attr2 (e.g., chol-level)
-
-



40
-

-

-

-

50
num. attr1 (e.g., age)
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Decision trees

• so we build a decision tree

agelt50
N
Y
chol. lt40

Y
N
-
...
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Decision trees

• Typically, two steps
• tree building
• tree pruning (for over-training/over-fitting)

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Tree building

• How?

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Tree building

• How?
• A Partition, recursively - pseudocode
• Partition ( dataset S)
• if all points in S have same label
• then return
• evaluate splits along each attribute A
• pick best split, to divide S into S1 and S2
• Partition(S1) Partition(S2)
• Q1 how to introduce splits along attribute Ai
• Q2 how to evaluate a split?

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Tree building

• Q1 how to introduce splits along attribute Ai
• A1
• for numerical attributes
• binary split, or
• multiple split
• for categorical attributes
• compute all subsets (expensive!), or
• use a greedy approach

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Tree building

• Q2 how to evaluate a split?
• A by how close to uniform each subset is - ie.,
  we need a measure of uniformity

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Tree building
Any other measure?
entropy H(p, p-)
p relative frequency of class in S
1
0
0.5
0
1
p
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Tree building
gini index 1-p2 - p-2
entropy H(p, p-)
p relative frequency of class in S
1
0
0.5
0
1
p
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Tree building

• Intuition
• entropy bits to encode the class label
• gini classification error, if we randomly guess
  with prob. p

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Tree building

• Thus, we choose the split that reduces
  entropy/classification-error the most E.g.

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Tree building

• Before split we need
• (n n-) H( p, p-) (76) H(7/13, 6/13)
• bits total, to encode all the class labels
• After the split we need
• 0 bits for the
  first half and
• (26) H(2/8, 6/8) bits for the second half

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Tree pruning

• What for?

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Tree pruning

• Q How to do it?

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Tree pruning

• Q How to do it?
• A1 use a training and a testing set - prune
  nodes that improve classification in the
  testing set. (Drawbacks?)

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Tree pruning

• Q How to do it?
• A1 use a training and a testing set - prune
  nodes that improve classification in the
  testing set. (Drawbacks?)
• A2 or, rely on MDL ( Minimum Description
  Language) - in detail

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Tree pruning

• envision the problem as compression
• and try to minimize the bits to compress
• (a) the class labels AND
• (b) the representation of the decision tree

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(MDL)

• a brilliant idea e.g. best n-degree polynomial
  to compress these points
• the one that minimizes (sum of errors n )

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Major Issues in Data Mining

• Issues relating to the diversity of data types
• Handling relational as well as 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