Decision support tools : Data warehousing, OLAP and data mining.

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Decision support tools Data warehousing, OLAP
and data mining.

• Sunita Sarawagi

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Data explosion

• Banks, companies, websites, retail stores,
  scientific labs --- contain terabytes of data
  and is continually growing.
• Storage and processing getting cheaper
• Wealth of information hidden in the flood of data
• Conventional querying/analysis methods did not
  scale
• Need new ways of interaction
• Data warehousing and Data mining

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What is a Data Warehouse?

• A single, complete and consistent store of data
  obtained from a variety of different sources made
  available to end users in a what they can
  understand and use in a business context.
• Barry Devlin

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Why a Warehouse?

• Large organizations have complex internal
  organizations, and have data stored at different
  locations, on different operational
• Data sources often store only current data, not
  historical data
• Corporate decision making requires a unified view
  of all organizational data, including historical
  data
• A data warehouse
• Greatly simplifies querying, permits study of
  historical trends
• Shifts decision support query load away from
  transaction processing systems

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Decision support tools
Mining tools
Direct Query
Reporting tools
Intelligent Miner
Essbase
Crystal reports
Merge Clean Summarize
Relational DBMS e.g. Redbrick
Data warehouse
Staging ETL layer
Detailed transactional data
Oracle
SAS
IMS
Operational data
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MTNL case study

• MTNL operates in 9 zones, each with 5-7
  exchanges.

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Data Warehouse vs. Operational DBMS

• OLTP (on-line transaction processing)
• Day-to-day operations purchasing, inventory,
  banking, manufacturing, payroll, registration,
  accounting, etc.
• Data warehouse system
• Data analysis and decision making
• Distinct features
• User and system orientation customer vs. market
• Data contents current, detailed vs. historical,
  consolidated
• Database design ER application vs. star
  subject
• View current, local vs. evolutionary, integrated
• Access patterns update vs. read-only but complex
  queries

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Data warehouse construction

• Heterogeneous schema integration
• merge from various sources, fuzzy matches
• remove inconsistencies
• Data cleaning
• missing data, outliers, clean fields e.g.
  names/addresses
• Data loading efficient parallel loads
• Products Prism warehouse manager, Platinum info
  refiner, info pump, QDB, Vality

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Warehouse maintenance

• Data refresh
• when to refresh, what form to send updates?
• Materialized view maintenance with batch updates.
• Query evaluation using materialized views
• Monitoring and reporting tools
• HP intelligent warehouse advisor

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Warehouse Schemas

• Typically warehouse data is multidimensional,
  with very large fact tables
• Examples of dimensions item-id, date/time of
  sale, store where sale was made, customer
  identifier
• Examples of measures number of items sold, price
  of items
• Dimension values are usually encoded using small
  integers and mapped to full values via dimension
  tables
• Resultant schema is called a star schema
• More complicated schema structures
• Snowflake schema multiple levels of dimension
  tables
• Constellation multiple fact tables

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OLAP

• Fast, interactive answers to large aggregate
  queries.
• Multidimensional model dimensions with
  hierarchies
• Dim 1 Bank location
• branch--gtcity--gtstate
• Dim 2 Customer
• sub profession --gt profession
• Dim 3 Time
• month --gt quarter --gt year
• Measures loan amount, transactions, balance

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Multidimensional Data

• Sales volume as a function of product, month, and
  region

Dimensions Product, Location, Time Hierarchical
summarization paths
Region
Industry Region Year Category
Country Quarter Product City Month
Week Office Day
Product
Month
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A Sample Data Cube
Total annual sales of TV in India
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Typical OLAP Operations

• Roll up (drill-up) summarize data
• by climbing up hierarchy or by dimension
  reduction
• Drill down (roll down) reverse of roll-up
• from higher level summary to lower level summary
  or detailed data, or introducing new dimensions
• Slice and dice
• project and select
• Pivot (rotate)
• reorient the cube, visualization, 3D to series of
  2D planes.
• Other operations
• drill across involving (across) more than one
  fact table
• drill through through the bottom level of the
  cube to its back-end relational tables (using SQL)

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OLAP products

• About 30 OLAP vendors
• Dominant ones
• Oracle Express largest market share
• Hyperion technology leader
• Microsoft Plato introduced late last year,
  rapidly taking over...

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Part 2 Data mining
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Data mining

• Process of semi-automatically analyzing large
  databases to find patterns that are
• valid hold on new data with some certainity
• novel non-obvious to the system
• useful should be possible to act on the item
• understandable humans should be able to
  interpret the pattern
• Other names Knowledge discovery in databases,
  Data analysis

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Relationship with other fields

• Overlaps with machine learning, statistics,
  artificial intelligence, databases, visualization
  but more stress on
• scalability of number of features and instances
• stress on algorithms and architectures whereas
  foundations of methods and formulations provided
  by statistics and machine learning.
• automation for handling large, heterogeneous data

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Applications

• Banking loan/credit card approval
• predict good customers based on old customers
• Customer relationship management
• identify those who are likely to leave for a
  competitor.
• Targeted marketing
• identify likely responders to promotions
• Fraud detection telecommunications, financial
  transactions
• from an online stream of event identify
  fraudulent events
• Manufacturing and production
• automatically adjust knobs when process parameter
  changes

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Applications (continued)

• Medicine disease outcome, effectiveness of
  treatments
• analyze patient disease history find
  relationship between diseases
• Molecular/Pharmaceutical identify new drugs
• Scientific data analysis
• identify new galaxies by searching for sub
  clusters
• Web site/store design and promotion
• find affinity of visitor to pages and modify
  layout

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Mining technology today
Preprocessing utilities
Mining operations
Data warehouse
Extract data via ODBC
Visualization Tools

• Sampling
• Attribute transformation

• Vendors
• (IDC 1999)
• SAS 29
• SPSS 13.5
• IBM 6

• Scalable algorithms
• association
• classification
• clustering
• sequence mining

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Mining operations

• Itemset mining
• Association rules
• Causality

• Clustering
• hierarchical
• EM
• density based

• Classification
• Regression
• Classification trees
• Neural networks
• Bayesian learning
• Nearest neighbour
• Radial basis functions
• Support vector machines
• Meta learning methods
• Bagging,boosting

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Classification

• Given old data about customers and payments,
  predict new applicants loan eligibility.

Previous customers
Classifier
Decision rules
Age Salary Profession Location Customer type
Salary gt 5 L
Good/ bad
Prof. Exec
New applicants data
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Classification methods

• Goal Predict class Ci f(x1, x2, .. Xn)
• Regression (linear or any other polynomial)
• Decision tree classifier divide decision space
  into piecewise constant regions.
• Neural networks partition by non-linear
  boundaries
• Probabilistic/generative models
• Lazy learning methods nearest neighbor
• Support vector machines boundary to maximally
  separate classes

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Decision tree classifiers

• Widely used learning method
• Easy to interpret can be re-represented as
  if-then-else rules
• Approximates function by piece wise constant
  regions
• Does not require any prior knowledge of data
  distribution, works well on noisy data.
• Has been applied to
• classify medical patients based on the disease,
• equipment malfunction by cause,
• loan applicant by likelihood of payment.

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Decision trees

• Tree where internal nodes are simple decision
  rules on one or more attributes and leaf nodes
  are predicted class labels.

Salary lt 1 M
Prof teacher
Age lt 30
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Algorithm for tree building

• Greedy top-down construction.

Gen_Tree (Node, data)
Yes
make node a leaf?
Stop
Selection criteria
Find best attribute and best split on attribute
Partition data on split condition
For each child j of node Gen_Tree (node_j,
data_j)
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Nearest neighbor

• Define proximity between instances, find
  neighbors of new instance
• K-NN approach assign majority class amongst k
  nearest neighbour
• weighted regression learn a new regression
  equation by weighting each training instance
  based on distance from new instance

• Cons
• Slow during application.
• No feature selection.
• Notion of proximity vague

• Pros
• Fast training

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Neural networks

• Useful for learning complex data like
  handwriting, speech and image recognition

Decision boundaries
Neural network
Classification tree
Linear regression
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Bayesian learning

• Assume a probability model on generation of data.
  
• Apply Bayes theorem to find most likely class as
• Naïve bayes Assume attributes conditionally
  independent given class value

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Meta learning methods

• No single classifier good under all cases
• Difficult to evaluate in advance the conditions
• Meta learning combine the effects of the
  classifiers
• Voting sum up votes of component classifiers
• Combiners learn a new classifier on the outcomes
  of previous ones
• Boosting staged classifiers
• Disadvantage interpretation hard
• Knowledge probing learn single classifier to
  mimick meta classifier

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What is Cluster Analysis?

• Cluster a collection of data objects
• Similar to one another within the same cluster
• Dissimilar to the objects in other clusters
• Cluster analysis
• Grouping a set of data objects into clusters
• Clustering is unsupervised classification no
  predefined classes
• Typical applications
• As a stand-alone tool to get insight into data
  distribution
• As a preprocessing step for other algorithms

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Applications

• Customer segmentation e.g. for targeted marketing
• Group/cluster existing customers based on time
  series of payment history such that similar
  customers in same cluster.
• Identify micro-markets and develop policies
  foreach
• Image processing
• Text clustering e.g. scatter/gather
• Compression

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Distance functions

• Numeric data euclidean, manhattan distances
• Minkowski metric sum(xi-yi)m(1/m)
• Larger m gives higher weight to larger distances
• Categorical data 0/1 to indicate
  presence/absence
• Euclidean distance equal weightage to 1 and 0
  match
• Hamming distance ( dissimilarity)
• Jaccard coefficients similarity in 1s/( of 1s)
  (0-0 matches not important
• data dependent measures similarity of A and B
  depends on co-occurance with C.
• Combined numeric and categorical dataweighted
  normalized distance

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Clustering methods

• Hierarchical clustering
• agglomerative Vs divisive
• single link Vs complete link
• Partitional clustering
• distance-based K-means
• model-based EM
• density-based

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Agglomerative Hierarchical clustering

• Given matrix of similarity between every point
  pair
• Start with each point in a separate cluster and
  merge clusters based on some criteria
• Single link merge two clusters such that the
  minimum distance between two points from the two
  different cluster is the least
• Complete link merge two clusters such that all
  points in one cluster are close to all points
  in the other.

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Partitional methods K-means

• Criteria minimize sum of square of distance
• Between each point and centroid of the cluster.
• Between each pair of points in the cluster
• Algorithm
• Select initial partition with K clusters random,
  first K, K separated points
• Repeat until stabilization
• Assign each point to closest cluster center
• Generate new cluster centers
• Adjust clusters by merging/splitting

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Association Rule

• Given (1) database of transactions, (2) each
  transaction is a list of items (purchased by a
  customer in a visit)
• Find all rules that correlate the presence of
  one set of items with that of another set of
  items
• Rule form Body Head support, confidence.
• E.g., 98 of people who have an checking account
  and a PPF also apply for a credit card

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Rule Measures Support and Confidence
Customer buys both

• Find all the rules X Y ? Z with minimum
  confidence and support
• support, s, probability that a transaction
  contains X Y Z
• confidence, c, conditional probability that a
  transaction having X Y also contains Z

Customer buys d
Customer buys b

• Let minimum support 50, and minimum confidence
  50, we have
• A ? C (50, 66.6)
• C ? A (50, 100)

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Applications of fast itemset counting

• Find correlated events
• Applications in medicine find redundant tests
• Cross selling in retail, banking
• Improve predictive capability of classifiers that
  assume attribute independence
• New similarity measures of categorical
  attributes Mannila et al, KDD 98

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Mining market

• Around 20 to 30 mining tool vendors
• Major tool players
• SASs Enterprise Miner.
• IBMs Intelligent Miner,
• SGIs MineSet,
• All pretty much the same set of tools
• Many embedded products
• fraud detection
• electronic commerce applications,
• health care,
• customer relationship management Epiphany

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Summary

• Need for new decision support tools
• Data warehousing
• Data integration, loading, cleaning
• Interactive data analysis/navigation OLAP
• Data mining definition and an overview of the
  various operations
• Classification regression, nearest neighbour,
  neural network, bayesian
• Clustering distance based (k-means),
  Heirarchical
• Itemset counting