Database%20Clustering%20and%20Summary%20Generation

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Database Clustering and Summary Generation

• Tae-Wan Ryu and Christoph F. Eick

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Similarity Measures For Multi-valued Attributes
for Database Clustering

• Tae-wan Ryu and Christoph F. Eick
• Department of Computer Science
• University of Houston
• Talk Organization
• Database Clustering
• Problems of Database Clustering
• Extended Data Sets
• Similarity Measures for Sets and Bags
• An Architecture for Database Clustering
• Summary and Conclusion

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General KDD Steps
Data sources
Selected/Preprocessed data
Transformed data
Extracted information
Knowledge
Select/preprocess
Transform
Data mine
Interpret/Evaluate/Assimilate
Data preparation
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Research Goal

• To develop methodologies, techniques, and tools
  to create summaries from databases using cluster
  analysis and genetic programming
• Our approach
• Partition the database into groups of similar
  objects using cluster analysis
• Find commonalities that objects belonging to each
  group share using genetic programming

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Database Summary Generation Steps and Example
lt Example gt
lt Steps gt
Database
Restaurant database
Database Clustering
Clusters
Groups of similar objects
White color
Retired
Young
Summary Generation
Midnight
Dinner
Lunch
Summaries describing the commonalities within
each group
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An Example Schema Diagram
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Preprocessing forDatabase Clustering

• Preparing input data sets for clustering
• Appropriate data selection and preparation from a
  database is important task
• Key Problems
• How to support a users viewpoint including
  attribute selection
• Data model discrepancy between storage format and
  the input format that clustering algorithms
  assume
• How to cope with structural information,
  especially 1n and nm relationships

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Input Format for Data Mining Algorithms

• Data Format for Input Data Sets
• Single flat file format (basically, the data set
  has to be stored as a single(!) relation)
• Complex and structured formats
• Problem Almost all existing data mining and
  clustering approaches assume that input data set
  is in single flat file format.

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An Example Database to Illustrate the Problems
with Relationship Information in Database
Clustering

• Person Purchase
  Joined result
• (a) (b)
• ptype (payment type) 1 for cash, 2 for
  credit, and 3 for check, the cardinality ratio is
  1n
• (a) an example of Personal relational database,
  (b) a joined table from Person and
  
  Purchase relations

ssn name age sex 111111111
Johny 43 M 222222222 Andy 21 F
333333333 Post 67 M 444444444 Jenny 35
F
ssn location ptype amount
date 111111111 Warehouse 1 400
02-10-96 111111111 Grocery 2
70 05-14-96 111111111 Mall
3 200 12-24-96 222222222 Mall
2 300 12-23-96
222222222 Grocery 3 100
06-22-96 333333333 Mall 1
30 11-05-96
name age sex ptype amount location
Johny 43 M 1 400 Mall
Johny 43 M 2 70 Grocery
Johny 43 M 3 200 Warehouse
Andy 42 F 2 300 Mall
Andy 42 F 3 100
Grocery Post 67 M 1 30
Mall Jenny 35 F null null
null
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Existing Approaches

• Applying aggregate functions or generalization
• operators to convert a multi-valued attribute
  into a single
• valued attribute.
• Problems
• User has to make a critical decision (e.g., which
  aggregate function to use?)
• Valuable related information may be lost.

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Extended Data Sets
name age sex ptype amount location
Johny 43 M 1 400 Mall
Johny 43 M 2 70 Grocery
Johny 43 M 3 200 Warehouse
Andy 42 F 2 100 Mall
Andy 42 F 3 100
Grocery Post 67 M 1 30
Mall Jenny 35 F null null
null

name age sex p.ptype p.amount
p.location Johny 43 M 1,2,3
400,70,200 Mall, Grocery, Warehouse Andy
21 F 2,3 100,100 Mall,
Grocery Post 67 M 1
30 Mall Jenny 35 F
null null null
A converted table with a bag of values
How to measure similarity between bags of values?

• Group similarity measures are needed.

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Approaches for Database Clustering
Structured database
Clustering algorithms
Manual transformation
Flat file
ltCurrent approachgt
Structured database
Extended data set
Generalized Clustering algorithms
Automated preprocessing
ltProposed approachgt
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Related Work

• LABYRINTH (Thompson et al.)
• Ketterlins extended COBWEB
• KATE (Manago et al.)
• SUBDUE (Holder et al.)
• INLEN (Ribeiro et al.)
• KBG (Bisson et al.), KLUSTER (Kietz et al.)

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Research Objectives for Database Clustering

• To alleviate the representational gab between
  databases on the one hand and input formats of
  clustering algorithms on the other hand
• To design and implement semi-automatic tools to
  facilitate database clustering
• To generalize clustering algorithms

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Generating Extended Data Sets Froma Structured
Database
Database d1, d2, , dn
Users interests and objectives
Extended data set generator
Extended data set1
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A Unified Similarity Measure for Clustering
Extended Data Sets

• Group Similarity Measures
• Mixed Types qualitative, quantitative types.
• Qualitative type Tverskys set-theoretical
  similarity models.
• Contrast model
• S(a,b) ?f(A?B) ? ?f(A ? B) ? ?f(B ? A),
• where a and b be two objects, and A and B denote
  the sets of features for some ?, ?, ? ? 0 f is
  the cardinality of the set
• Ratio model (e.g., normalized similarity)
• S(a,b) f(A?B) / f(A?B) ?f(A ? B) ?f(B ?
  A), ?, ? ? 0

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Group Similarity Measures... continued

• Quantitative type group average
• Group average between group A and B
• where n is the total number of object-pairs,
  d(a,b)i is the dissimilarity measure for the ith
  pair of objects a and b,
• a ? A, b ? B.
• By taking the average of all the inter-object
  measures for those pairs of
• objects from which each object of a pair is in
  different groups.

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A Framework for Mixed Type Similarity Measures
for Extended Data Sets

• Gowers similarity measure for data sets with
  mixed-types.
• Extended similarity measure for multi-valued data
  sets with mixed-types.
• where m l q. The functions, sl(a,b) and
  sq(a,b) are similarity functions for qualitative
  attributes and quantitative attributes
  respectively.

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Clustering Algorithms for Extended Data Sets

• Nearest-neighbor clustering
• DBSCAN
• Leader algorithm
• Hierarchical clustering

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Database Clustering Environment
A set of clusters
Library of clustering algorithms
Extended Data set
Similarity measure
Clustering Tool
Library of similarity measures
Similarity Measure Tool
Data Extraction Tool
User Interface
Type and weight information
Default choice and domain information
DBMS
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A More Detailed Tool Architecture
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A Join Template Form
A Join Template Form Begin-spec
Database-name DB Link-definitions Link-list
Begin-join Dataset-of-interest
Dsetintrest Selected-attributes
Attr-list Objective-attributes
Obj-attr-list Extended-data-set E
End-join End-spec
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An Example of the Interface of the Extended Data
Set Generation Tool
Begin-spec DB-name Company
Link-definitions superv(Employee.ssn,
Employee.superssn), husband(Employee.ssn
, Marriage.hssn), wife(Employee.ssn,Marri
age.wssn), ehusband(Marriage.hssn,
Employee.ssn), ewife(Marriage.wssn,
Employee.ssn), works_on(Employee.ssn,
Works_on.essn), project(Works_on.pno,
Project.pnum), works_for(Employee.dno,
Department.dnum), works_loc(Department.dnu
m, Dept_loc.dnum) Begin-join
Dateset-of-interest Employee
Selected-attributes ssn, sex, salary,
superv.salary, wife.ewife.salary,
works_on.hours, works_on.project.pname,
works_for.works_loc.dloc
Objective-attributes ssn Output-data-set
E1 End-join End-spec
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Algorithm to Generate Extended Data Sets

• Project the Data Set of Interest by Primary key
  and Selected Attributes
• Join the Data set of Interest and related data
  sets to get all related attributes for each
  join-path
• Group attributes together that describe the same
  object

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Summary Representation

• Our approach uses database queries as our summary
  representation language.
• Queries that compute the objects belonging to a
  cluster and no other objects are considered to be
  perfect summaries for a cluster.
• An example query for a cluster
• (SELECT ssn name address
• FROM person purchase
• WHERE (amount-spent gt 1000) and
• (payment-type cash)and
• (store-name flea-market))
• Typically, members in the cluster have spent
  more than
• 1,000 cash for shopping in a flea-market

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Summary and Contributions

• Discussed the data model discrepancy between
  database storage format and input data format for
  traditional clustering algorithms
• Discussed the problems of dealing with
  relationship information in database clustering
• Presented a different way of representing related
  information using extended data sets
• Introduced the design and architecture of an
  automatic tools to generate extended data sets
  from databases
• Generalized the traditional similarity measures
  and present a framework to cope with extended
  data sets in similarity-based clustering

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Architecture of MASSON

g1
cluster
Clustering module
g2
...
Schema information
Object set
gk
user input
system input
user interface
GP based discovery system
generate
apply
DBMS
DB
select
Query set
Interface
user input
KB
GP engine
Domain knowledge
Query result
return
evaluate
system input
Discovered query set
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Evolution Process
Generationn
Initial generation
generation2
evolve
evolve
evolved population
Initial population
evolved population
qn1, qn2,..,qnm
q11, q12,..,q1m
q21, q22,..,q2m
selection crossover mutation
selection crossover mutation
selection
Solution Q
n number of generation m the size of population
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Evolution Process
Generationn
Initial generation
generation2
evolve
evolve
evolved population
Initial population
evolved population
qn1, qn2,..,qnm
q11, q12,..,q1m
q21, q22,..,q2m
selection crossover mutation
selection crossover mutation
selection
Solution Q
n number of generation m the size of population