Download Data Mining Concept Animation Library

Data Mining
Concept Animation Library
By
Nisarg Shah
Project Advisor
Dr. Meiliu Lu
Department of Computer Science
California State University, Sacramento
Spring 2008
Agenda
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2
Motivation
Scope of the Project
Background Knowledge
Approach
Implementation Details
Demo
Lessons Learned / Reinforced
Future work
References
Q&A / Feedback
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Data Mining Concept Animation
Library
Motivation
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CSc177 – Data Mining courseware
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Idea from another Masters project
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Operating System Concept Animation Library [2]
Own experience
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Another students’ work in CSc212 courseware [1]
Something that helps students learning the material better
Having gone through pain of understanding Data Mining
algorithms myself
Not much of Graphical & Interactive stuff available outside
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Data Mining Concept Animation
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Scope of the Project
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Data Mining Concept Animation Library
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A collection of Data Mining algorithms with graphical and
interactive user interface
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Basic idea: Students can learn, understand and compare
different algorithms
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Plenty of algorithms and impossible to cover all of them
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Just a start …
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Apriori algorithm
Frequent Patten (FP) Growth algorithm
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Data Mining Concept Animation
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Background Knowledge
Apriori Algorithm
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Apriori pruning principle:
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If there is any itemset which is infrequent, its superset
should not be generated/tested!
Method:
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Initially, scan DB once to get frequent 1-itemset
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Generate length (k+1) candidate itemsets from length k frequent
itemsets
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Step 1: self-joining Lk
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Step 2: pruning
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Test the candidates against DB
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Terminate when no frequent or candidate set can be generated
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Data Mining Concept Animation
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Background Knowledge (2)
Apriori Example
Database (Supmin = 2)
Tid
10
20
30
40
L2
Items
A, C, D
B, C, E
A, B, C, E
B, E
Itemset
{A, C}
{B, C}
{B, E}
{C, E}
C3
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C1
1st scan
sup
2
2
3
2
Itemset
{B, C, E}
C2
Itemset
{A}
{B}
{C}
sup
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3
3
{D}
{E}
1
3
Itemset
{A, B}
{A, C}
{A, E}
{B, C}
{B, E}
{C, E}
3rd scan
L1
sup
1
2
1
2
3
2
L3
Itemset
{A}
{B}
{C}
{E}
C2
nd
2 scan
Itemset
{B, C, E}
sup
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sup
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Itemset
{A, B}
{A, C}
{A, E}
{B, C}
{B, E}
{C, E}
Background Knowledge (3)
Frequent Pattern Growth Algorithm
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Mining Frequent Patterns Without Candidate Generation
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Two-step method
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Construct FP-tree from a Transaction Database
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Find Patterns Having P From P-conditional Database
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Scan DB once, find frequent 1-itemset (single item pattern)
Sort frequent items in frequency descending order, f-list
Scan DB again, construct FP-tree
Starting at the frequent item header table in the FP-tree
Traverse the FP-tree by following the link of each frequent item p
Accumulate all of transformed prefix paths of item p to form p’s
conditional pattern base
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Data Mining Concept Animation
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Background Knowledge (4)
FP Growth Example – Step 1
Construct FP-tree from a Transaction Database
TID
Items bought
Header Table
100
200
300
400
500
{f, a, c, d, g, i, m, p}
{a, b, c, f, l, m, o}
{b, f, h, j, o, w}
{b, c, k, s, p}
{a, f, c, e, l, p, m, n}
Item frequency head
f
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c
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a
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b
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m
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p
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(ordered) frequent items
100 {f, c, a, m, p}
200 {f, c, a, b, m}
min_support = 3
300 {f, b}
400 {c, b, p}
F-list=f-c-a-b-m-p
500 {f, c, a, m, p}
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{}
f:4
c:3
c:1
b:1
a:3
b:1
p:1
m:2
b:1
p:2
m:1
Data Mining Concept Animation
Library
Background Knowledge (5)
FP Growth Example – Step 2
Find Patterns Having P From P-conditional Database
{}
Conditional pattern bases
Header Table
Item frequency head
f
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c
4
a
3
b
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m
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p
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9
f:4
c:3
c:1
b:1
a:3
p:1
m:2
b:1
p:2
m:1
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b:1
item
cond. pattern base
c
f:3
a
fc:3
b
fca:1, f:1, c:1
m
fca:2, fcab:1
p
fcam:2, cb:1
Data Mining Concept Animation
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Approach
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Gathering requirements
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In-depth understanding of the two algorithms
Input from students/professor (couldn’t really do this in
detail)
Choosing the appropriate tools for implementation
Feasibility test
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Starting point – Algorithm pseudo code
Console based application
Dynamic algorithms in nature
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With configurable files for input parameters and transaction
details
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Approach (2)
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GUI Application
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Choosing right technology
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Choosing right components
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Setting limitations (max items, max transactions)
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Simple and consistent background
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Multiple algorithms under different tabs
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User can match output against each other
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Implementation Details
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Why Applets?
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An Applet is an program written in Java programming language
that can be included in an HTML page
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Works well with Java technology-enabled web browser
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It can run at a comparable speed to other compiled languages
such as C++, but many times faster than JavaScript
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It can move the work from the server to the client, making a web
solution more scalable with the number of users/clients
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Data Mining Concept Animation
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Implementation Details (2)
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Problem - Counting support of Candidates
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How many times a particular candidate itemset (of
any length) appears in the transaction table?
Solution
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All possible candidate itemsets are generated and
calculated only once – at the beginning
Candidate itemsets are stored in a Hash
table(key,val) pair
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Key=candidate itemset; val=count
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Implementation Details (3)
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Use of third party tool
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Combination generator [4]
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Generates all possible combinations of given size
for given itemset
Example: Given Itemset {A,B,C,D}
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All possible 3-itemsets: {A,B,C}, {A,B,D}, {A,C,D},
{B,C,D}
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Implementation Details (4)
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Choosing between components
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Purpose: display and edit regular 2-D tables of cells
Options: JTable Vs JEditorPane
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JTable
– Rendering problem:
refreshing screen on
detecting table selections
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JEditorPane
– With the use of
JCheckboxes
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Data Mining Concept Animation
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Implementation Details (5)
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Problem: How to display FP-tree?
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Solution: JGraph
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Takes the description of a graph as
input, and produces a graph display
on the standard output
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Data Mining Concept Animation
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Lessons Learned/Reinforced
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Don’t procrastinate (specially if you’re working fulltime)
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Clear understanding of Apriori & FP growth algorithms
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A good programming experience with Java and Applets
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Demonstrations/graphical tools are useful for explaining
concepts
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Simple project idea can be meaningful
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Data Mining Concept Animation
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Future Work
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Get feedback and recommendations from potential
users (students & instructor)
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An open source library
Data Mining Concept Animation Library
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A comparison between multiple algorithms on same
set of data
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Potential idea for a course project or
Bachelors/Masters project
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Data Mining Concept Animation
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References
1.
XML Data Representation and Transformations for
Bioinformatics
http://athena.ecs.csus.edu/~woodsk/courseware/
2.
Operating System Concept Animation Library
http://gaia.ecs.csus.edu/%7Ezhangd/oscal/oscal.htm
3.
Data Mining Concepts and Techniques book
by Jiawei Han and Micheline Kamber
4.
Combination generator tool
http://www.merriampark.com/comb.htm
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Data Mining Concept Animation
Library
Q&A / Feedback
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Questions ??
Feedback
– Useful or not?
– Any recommendations / suggestions?
– Which algorithms/concepts from current
material you would like to be implemented
in such a tool?
Thank you!!!
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Data Mining Concept Animation
Library