CSE 634
Data Mining Concepts and Techniques
Association Rule Mining
Barbara Mucha
Tania Irani
Irem Incekoy
Mikhail Bautin
Course Instructor: Prof. Anita Wasilewska
State University of New York, Stony Brook
Group 6
References





Data Mining: Concepts & Techniques by Jiawei
Han and Micheline Kamber
Presentation Slides of Prateek Duble
Presentation Slides of the Course Book.
Mining Topic-Specific Concepts and Definitions
on the Web
Effective Personalization Based on Association
Rule Discovery from Web Usage Data
Overview

Basic Concepts of Association Rule
Mining
 Association
& Apriori Algorithm
Paper: Mining Topic-Specific Concepts
and Definitions on the Web
 Paper: Effective Personalization Based on
Association Rule Discovery from Web
Usage Data

Barbara Mucha
Outline

What is association rule mining?

Methods for association rule mining

Examples

Extensions of association rule
Barbara Mucha
What Is Association Rule Mining?

Frequent patterns: patterns (set of items,
sequence, etc.) that occur frequently in a
database

Frequent pattern mining: finding regularities in
data
 What products were
 Beer and diapers?!
 What
often purchased together?
are the subsequent purchases after buying a
car?
 Can we automatically profile customers?
Barbara Mucha
Basic Concepts of Association Rule
Mining


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


E.g., 98% of people who purchase tires and auto accessories also
get automotive services done
Applications



*  Maintenance Agreement (What the store should do to boost
Maintenance Agreement sales)
Home Electronics  * (What other products should the store
stocks up?)
Attached mailing in direct marketing
Barbara Mucha
Association Rule Definitions





Set of items: I={I1,I2,…,Im}
Transactions: D = {t1, t2,.., tn} be a set of
transactions, where a transaction,t, is a set of
items
Itemset: {Ii1,Ii2, …, Iik}  I
Support of an itemset: Percentage of transactions
which contain that itemset.
Large (Frequent) itemset: Itemset whose number
of occurrences is above a threshold.
Barbara Mucha
Rule Measures: Support &
Confidence

An association rule is of the form : X  Y where X, Y are
subsets of I, and X INTERSECT Y = EMPTY

Each rule has two measures of value, support, and confidence.

Support indicates the frequencies of the occurring patterns, and
confidence denotes the strength of implication in the rule.

The support of the rule X  Y is support (X UNION Y) c is the
CONFIDENCE of rule X  Y if c% of transactions that
contain X also contain Y, which can be written as the radio:

support(X UNION Y)/support(X)
Barbara Mucha
Support & Confidence : An
Example
Let minimum support 50%, and minimum
confidence 50%, then we have,
 A  C (50%, 66.6%)
 C  A (50%, 100%)
TransactionID ItemsBought
2000
A,B,C
1000
A,C
4000
A,D
5000
B,E,F
Barbara Mucha
Types of Association Rule Mining

Boolean vs. quantitative associations
(Based on the types of values handled)
 buys(x, “computer”)  buys(x, “financial software”)
[.2%, 60%]
 age(x, “30..39”) ^ income(x, “42..48K”) buys(x,
“PC”) [1%, 75%]

Single dimension vs. multiple dimensional associations
 buys(x, “computer”)  buys(x, “financial software”)
[.2%, 60%]
 age(x, “30..39”) ^ income(x, “42..48K”) buys(x,
“PC”) [1%, 75%]
Barbara Mucha
Types of Association Rule Mining

Single level vs. multiple-level analysis
 What brands of beers are associated with
what brands of diapers?

Various extensions
 Correlation, causality analysis
 Association
does not necessarily imply
correlation or causality
 Constraints
 E.g.,
enforced
small sales (sum < 100) trigger big buys
(sum > 1,000)?
Barbara Mucha
Association Discovery

Given a user specified minimum support (called MINSUP)
and minimum confidence (called MINCONF), an important

PROBLEM is to find all high confidence, large itemsets
(frequent sets, sets with high support). (where support and
confidence are larger than minsup and minconf).

This problem can be decomposed into two subproblems:

1. Find all large itemsets: with support > minsup (frequent
sets).

2. For a large itemset, X and B X (or Y  X) , find those rules,
X\{B} => B ( X-Y  Y) for which confidence > minconf.
Barbara Mucha
Basics

Itemset: a set of items
 E.g.,

acm={a, c, m}
Support of itemsets
 Sup(acm)=3


Given min_sup=3, acm
is a frequent pattern
Frequent pattern
mining: find all
frequent patterns in a
database
Barbara Mucha
Transaction database TDB
TID
100
200
300
400
500
Items bought
f, a, c, d, g, I, m, p
a, b, c, f, l,m, o
b, f, h, j, o
b, c, k, s, p
a, f, c, e, l, p, m, n
Mining Association Rules—An
Example
Transaction ID
2000
1000
4000
5000
Items Bought
A,B,C
A,C
A,D
B,E,F
For rule A  C:
Min. support 50%
Min. confidence 50%
Frequent Itemset Support
{A}
75%
{B}
50%
{C}
50%
{A,C}
50%
support = support({A &C}) = 50%
confidence = support({A &C})/support({A}) = 66.6%
The Apriori principle:
Any subset of a frequent itemset must be frequent
Rules from frequent sets
X = {mustard, sausage, beer}; frequency =
0.4
 Y = {mustard, sausage, beer, chips};
frequency = 0.2
 If the customer buys mustard, sausage,
and beer, then the probability that he/she
buys chips is 0.5

Barbara Mucha
Applications

Mine:
 Sequential patterns
 find inter-transaction patterns such that the presence of a set
of items is followed by another item in the time-stamp
ordered transaction set.
 Periodic patterns
 It can be envisioned as a tool for forecasting and prediction
of the future behavior of time-series data.
 Structural Patterns
 Structural patterns describe how classes and objects can be
combined to form larger structures.
Barbara Mucha
Application Difficulties



Wal-Mart knows that customers who buy Barbie
dolls have a 60% likelihood of buying one of three
types of candy bars.
What does Wal-Mart do with information like
that? 'I don't have a clue,' says Wal-Mart's chief of
merchandising, Lee Scott
www.kdnuggets.com/news/98/n01.html
Diapers and beer urban legend
http://web.onetel.net.uk/~hibou/Beer%20and%
20Nappies.html
Barbara Mucha
Thank You!
Barbara Mucha
CSE 634
Data Mining Concepts and Techniques
Association & Apriori Algorithm
Tania Irani
(105573836)
Course Instructor: Prof. Anita Wasilewska
State University of New York, Stony Brook
References

Data Mining: Concepts & Techniques by Jiawei
Han and Micheline Kamber

Presentation Slides of Prof. Anita Wasilewska
Agenda

The Apriori Algorithm (Mining single-dimensional
boolean association rules)

Frequent-Pattern Growth (FP-Growth) Method

Summary
The Apriori Algorithm: Key Concepts

K-itemsets: An itemset having k items in it.

Support or Frequency: Number of transactions that contain a
particular itemset.

Frequent Itemsets: An itemset that satisfies minimum support.
(denoted by Lk for frequent k-itemset).

Apriori Property: All non-empty subsets of a frequent itemset must
be frequent.

Join Operation: Ck, the set of candidate k-itemsets is generated by
joining Lk-1 with itself. (L1: frequent 1-itemset, Lk: frequent k-itemset)

Prune Operation: Lk, the set of frequent k-itemsets is extracted from
Ck by pruning it – getting rid of all the non-frequent k-itemsets in Ck
Iterative level-wise approach: k-itemsets used to explore (k+1)itemsets.
The Apriori Algorithm finds frequent k-itemsets.
How is the Apriori Property used in the
Algorithm?

Mining single-dimensional Boolean association
rules is a 2 step process:
 Using

the Apriori Property find the frequent itemsets:
Each iteration will generate Ck (candidate k-itemsets from
Ck-1) and Lk (frequent k-itemsets)
 Use
the frequent k-itemsets to generate association
rules.
Finding frequent itemsets using the Apriori
Algorithm: Example
TID
T100
List of Items

I1, I2, I5

T100
I2, I4
T100
I2, I3

T100
I1, I2, I4

T100
I1, I3

T100
I2, I3
T100
I1, I3

T100
I1, I2 ,I3, I5
T100
I1, I2, I3
Consider a database D, consisting
of 9 transactions.
Each transaction is represented
by an itemset.
Suppose min. support required is
2 (2 out of 9 = 2/9 =22 % )
Say min. confidence required is
70%.
We have to first find out the
frequent itemset using Apriori
Algorithm.
Then, Association rules will be
generated using min. support &
min. confidence.
Step 1: Generating candidate and frequent 1itemsets with min. support = 2
Scan D for
count of each
candidate
Itemset
Sup.Count
{I1}
6
{I2}
Compare candidate
support count with
minimum support
count
Itemset
Sup.Count
{I1}
6
7
{I2}
7
{I3}
6
{I3}
6
{I4}
2
{I4}
2
{I5}
2
{I5}
2
C1
L1
 In the first iteration of the algorithm, each item is a member of the set
of candidates Ck along with its support count.
 The set of frequent 1-itemsets L1, consists of the candidate 1itemsets satisfying minimum support.
Step 2: Generating candidate and frequent 2itemsets with min. support = 2
Generate C2
candidates
from L1 x L1
Itemset
Scan D for
count of
each
candidate
Itemset
Sup.
Count
{I1, I2}
4
{I1, I4}
{I1, I3}
4
{I1, I5}
{I1, I4}
1
{I2, I3}
{I1, I5}
2
{I2, I4}
{I2, I3}
4
{I2, I5}
{I2, I4}
2
{I3, I4}
{I2, I5}
2
{I3, I4}
0
{I3, I5}
1
{I4, I5}
0
{I1, I2}
{I1, I3}
{I3, I5}
{I4, I5}
C2
C2
Compare
candidate
support
count with
minimum
support
count
Itemset
Sup
Count
{I1, I2}
4
{I1, I3}
4
{I1, I5}
2
{I2, I3}
4
{I2, I4}
2
{I2, I5}
2
L2
Note: We haven’t used
Apriori Property yet!
Step 3: Generating candidate and frequent 3itemsets with min. support = 2
Generate
C3
candidates
from L2
Itemset
Scan D for
count of
each
candidate
Itemset
Sup.
Count
{I1, I2, I5}
{I1, I2, I3}
2
{I1, I3, I5}
{I1, I2, I5}
2
{I1, I2, I3}
{I2, I3, I4}
{I2, I3, I5}
{I2, I4, I5}
C3
C3
Compare
candidate
support
count with
min support
count
Itemset
Sup
Count
{I1, I2, I3}
2
{I1, I2, I5}
2
L3
Contains non-frequent
(2-itemset) subsets
 The generation of the set of candidate 3-itemsets C3, involves use of
the Apriori Property.
 When Join step is complete, the Prune step will be used to reduce the
size of C3. Prune step helps to avoid heavy computation due to large Ck.
Step 4: Generating frequent 4-itemset

L3 Join L3
C4 = {{I1, I2, I3, I5}}

This itemset is pruned since its subset {{I2, I3, I5}} is not
frequent.

Thus, C4 = φ, and the algorithm terminates, having found
all of the frequent items.

This completes our Apriori Algorithm. What’s Next ?

These frequent itemsets will be used to generate strong
association rules (where strong association rules satisfy
both minimum support & minimum confidence).
Step 5: Generating Association Rules from
frequent k-itemsets

Procedure:

For each frequent itemset l, generate all nonempty subsets of l

For every nonempty subset s of l, output the rule “s  (l - s)” if
support_count(l) / support_count(s) ≥ min_conf where min_conf is
minimum confidence threshold. 70% in our case.

Back To Example:

Lets take l = {I1,I2,I5}

The nonempty subsets of Lets take l are {I1,I2}, {I1,I5}, {I2,I5},
{I1}, {I2}, {I5}
Step 5: Generating Association Rules from
frequent k-itemsets [Cont.]

The resulting association rules are:
 R1:


Confidence = sc{I1,I2,I5} / sc{I1,I2} = 2/4 = 50%
R1 is Rejected.
 R2:



I1 ^ I5  I2
Confidence = sc{I1,I2,I5} / sc{I1,I5} = 2/2 = 100%
R2 is Selected.
 R3:

I1 ^ I2  I5
I2 ^ I5  I1
Confidence = sc{I1,I2,I5} / sc{I2,I5} = 2/2 = 100%
R3 is Selected.
Step 5: Generating Association Rules from
Frequent Itemsets [Cont.]



R4: I1  I2 ^ I5
 Confidence = sc{I1,I2,I5} / sc{I1} = 2/6 = 33%
 R4 is Rejected.
R5: I2  I1 ^ I5
 Confidence = sc{I1,I2,I5} / {I2} = 2/7 = 29%
 R5 is Rejected.
R6: I5  I1 ^ I2
 Confidence = sc{I1,I2,I5} / {I5} = 2/2 = 100%
 R6 is Selected.
We have found three strong association rules.
Agenda

The Apriori Algorithm (Mining single dimensional
boolean association rules)

Frequent-Pattern Growth (FP-Growth) Method

Summary
Mining Frequent Patterns Without Candidate
Generation


Compress a large database into a compact, FrequentPattern tree (FP-tree) structure

Highly condensed, but complete for frequent pattern mining

Avoid costly database scans
Develop an efficient, FP-tree-based frequent pattern mining method

A divide-and-conquer methodology:




Compress DB into FP-tree, retain itemset associations
Divide the new DB into a set of conditional DBs – each
associated with one frequent item
Mine each such database seperately
Avoid candidate generation
FP-Growth Method : An Example
TID
List of Items
T100
I1, I2, I5
T100
I2, I4
T100
I2, I3
T100
I1, I2, I4
T100
I1, I3
T100
I2, I3




T100
I1, I3
T100
I1, I2 ,I3, I5

T100
I1, I2, I3
Consider the previous example
of a database D, consisting of
9 transactions.
Suppose min. support count
required is 2 (i.e. min_sup =
2/9 = 22 % )
The first scan of the database
is same as Apriori, which
derives the set of 1-itemsets &
their support counts.
The set of frequent items is
sorted in the order of
descending support count.
The resulting set is denoted as
L = {I2:7, I1:6, I3:6, I4:2, I5:2}
FP-Growth Method: Construction of FP-Tree







First, create the root of the tree, labeled with “null”.
Scan the database D a second time (First time we scanned it to
create 1-itemset and then L), this will generate the complete tree.
The items in each transaction are processed in L order (i.e. sorted
order).
A branch is created for each transaction with items having their
support count separated by colon.
Whenever the same node is encountered in another transaction, we
just increment the support count of the common node or Prefix.
To facilitate tree traversal, an item header table is built so that each
item points to its occurrences in the tree via a chain of node-links.
Now, The problem of mining frequent patterns in database is
transformed to that of mining the FP-Tree.
FP-Growth Method: Construction of FP-Tree
null{}
Item
Id
Sup NodeCount link
I2
7
I1
6
I3
6
I4
2
I5
2
I2:7
I1:4
I3:2
I1:2
I4:1
I3:2
I3:2
I4:1
I5:1
I5:1
An FP-Tree that registers compressed, frequent pattern
information
Mining the FP-Tree by Creating Conditional
(sub) pattern bases
1.
2.
3.
4.
5.
Start from each frequent length-1 pattern (as an initial
suffix pattern).
Construct its conditional pattern base which consists of
the set of prefix paths in the FP-Tree co-occurring with
suffix pattern.
Then, construct its conditional FP-Tree & perform
mining on this tree.
The pattern growth is achieved by concatenation of the
suffix pattern with the frequent patterns generated from
a conditional FP-Tree.
The union of all frequent patterns (generated by step
4) gives the required frequent itemset.
FP-Tree Example Continued
Item
Conditional pattern base
Conditional
FP-Tree
Frequent pattern
generated
I5
{(I2 I1: 1),(I2 I1 I3: 1)}
<I2:2 , I1:2>
I2 I5:2, I1 I5:2, I2 I1 I5: 2
I4
{(I2 I1: 1),(I2: 1)}
<I2: 2>
I2 I4: 2
I3
{(I2 I1: 2),(I2: 2), (I1: 2)}
<I2: 4, I1: 2>,<I1:2>
I2 I3:4, I1 I3: 2 , I2 I1 I3: 2
I1
{(I2: 4)}
<I2: 4>
I2 I1: 4
Mining the FP-Tree by creating conditional (sub) pattern bases
Now, following the above mentioned steps:
 Lets start from I5. I5 is involved in 2 branches namely {I2 I1 I5: 1} and {I2 I1 I3
I5: 1}.
 Therefore considering I5 as suffix, its 2 corresponding prefix paths would be
{I2 I1: 1} and {I2 I1 I3: 1}, which forms its conditional pattern base.
FP-Tree Example Continued





Out of these, only I1 & I2 is selected in the conditional FP-Tree
because I3 does not satisfy the minimum support count.
For I1, support count in conditional pattern base = 1 + 1 = 2
For I2, support count in conditional pattern base = 1 + 1 = 2
For I3, support count in conditional pattern base = 1
Thus support count for I3 is less than required min_sup which is 2
here.
Now, we have a conditional FP-Tree with us.
All frequent pattern corresponding to suffix I5 are generated by
considering all possible combinations of I5 and conditional FP-Tree.
The same procedure is applied to suffixes I4, I3 and I1.
Note: I2 is not taken into consideration for suffix because it doesn’t
have any prefix at all.
Why Frequent Pattern Growth Fast ?

Performance study shows
 FP-growth
is an order of magnitude faster than
Apriori

Reasoning
 No
candidate generation, no candidate test
 Use
compact data structure
 Eliminate
 Basic
repeated database scans
operation is counting and FP-tree building
Agenda

The Apriori Algorithm (Mining single
dimensional boolean association rules)

Frequent-Pattern Growth (FP-Growth)
Method

Summary
Summary

Association rules are generated from frequent itemsets.

Frequent itemsets are mined using Apriori algorithm or FrequentPattern Growth method.

Apriori property states that all the subsets of frequent itemsets must
also be frequent.

Apriori algorithm uses frequent itemsets, join & prune methods and
Apriori property to derive strong association rules.

Frequent-Pattern Growth method avoids repeated database
scanning of Apriori algorithm.

FP-Growth method is faster than Apriori algorithm.
Thank You!
Mining Topic-Specific Concepts and
Definitions on the Web
Irem Incekoy
May 2003, Proceedings of the 12th International
conference on World Wide Web, ACM Press
Bing Liu, University of Illinois at Chicago, 851 S. Morgan
Street Chicago IL 60607-7053
Chee Wee Chin,
Hwee Tou Ng, National University of Singapore
3 Science Drive 2 Singapore
References

Agrawal, R. and Srikant, R. “Fast Algorithm for
Mining Association Rules”, VLDB-94, 1994.

Anderson, C. and Horvitz, E. “Web Montage: A
Dynamic Personalized Start Page”, WWW-02,
2002.

Brin, S. and Page, L. “The Anatomy of a LargeScale Hypertextual Web Search Engine”,
WWW7, 1998.
Introduction
When one wants to learn about a topic,
one reads a book or a survey paper.
 One can read the research papers about
the topic.
 None of these is very practical.
 Learning from web is convenient, intuitive,
and diverse.

Purpose of the Paper

This paper’s task is “mining topic-specific
knowledge on the Web”.

The goal is to help people learn in-depth
knowledge of a topic systematically on the
Web.
Learning about a New Topic
One needs to find definitions and
descriptions of the topic.
 One also needs to know the sub-topics
and salient concepts of the topic.
 Thus, one wants the knowledge as
presented in a traditional book.
 The task of this paper can be summarized
as “compiling a book on the Web”.

Proposed Technique

First, identify sub-topics or salient
concepts of that specific topic.

Then, find and organize the informative
pages containing definitions and
descriptions of the topic and sub-topics.
Why are the current search
tecnhiques not sufficient?

For definitions and descriptions of the topic:
Existing search engines rank web pages based on
keyword matching and hyperlink structures. NOT very
useful for measuring the informative value of the page.

For sub-topics and salient concepts of the topic:
A single web page is unlikely to contain information
about all the key concepts or sub-topics of the topic.
Thus, sub-topics need to be discovered from multiple
web pages. Current search engine systems do not
perform this task.
Related Work




•
Web information extraction wrappers
Web query languages
User preference approach
Question answering in information retrieval
Question answering is a closely-related work to this
paper. The objective of a question-answering system is
to provide direct answers to questions submitted by the
user. In this paper’s task, many of the questions are
about definitions of terms.
The Algorithm
WebLearn (T)
1) Submit T to a search engine, which returns a set of relevant pages
2) The system mines the sub-topics or salient concepts of T using a set
S of top ranking pages from the search engine
3) The system then discovers the informative pages containing
definitions of the topic and sub-topics (salient concepts) from S
4) The user views the concepts and informative pages.
If s/he still wants to know more about sub-topics then
for each user-interested sub-topic Ti of T do
WebLearn (Ti);
Sub-Topic or Salient Concept
Discovery

Observation:
Sub-topics or salient concepts of a topic are
important word phrases, usually emphasized
using some HTML tags (e.g.,
<h1>,...,<h4>,<b>).

However, this is not sufficient. Data mining
techniques are able to help to find the frequent
occurring word phrases.
Sub-Topic Discovery

After obtaining a set of relevant topranking pages (using Google), sub-topic
discovery consists of the following 5 steps.
1) Filter out the “noisy” documents that
rarely contain sub-topics or salientconcepts. The resulting set of documents
is the source for sub-topic discovery.
Sub-Topic Discovery
2) Identify important phrases in each page (discover
phrases emphasized by HTML markup tags).





Rules to determine if a markup tag can safely be ignored
Contains a salutation title (Mr, Dr, Professor).
Contains an URL or an email address.
Contains terms related to a publication (conference,
proceedings, journal).
Contains an image between the markup tags.
Too lengthy (the paper uses 15 words as the upper limit)
Sub-Topic Discovery

Also, in this step, some preprocessing
techniques such as stopwords removal
and word stemming are applied in order to
extract quality text segments.

Stopwords removal: Eliminating the words that occur
too frequently and have little informational meaning.
Word stemming: Finding the root form of a word by
removing its suffix.

Sub-Topic Discovery

3) Mine frequent occurring phrases:
- Each piece of text extracted in step 2 is stored in a
dataset called a transaction set.
- Then, an association rule miner based on Apriori
algorithm is executed to find those frequent itemsets. In
this context, an itemset is a set of words that occur
together, and an itemset is frequent if it appears in more
than two documents.
- We only need the first step of the Apriori algorithm and
we only need to find frequent itemsets with three words
or fewer (this restriction can be relaxed).
Sub-Topic Discovery

4) Eliminate itemsets that are unlikely to
be sub-topics, and determine the
sequence of words in a sub-topic.
(postprocessing)

Heuristic: If an itemset does not appear alone
as an important phrase in any page, it is unlikely
to be a main sub-topic and it is removed.
Sub-Topic Discovery

5) Rank the remaining itemsets. The
remaining itemsets are regarded as the
sub-topics or salient concepts of the
search topic and are ranked based on the
number of pages that they occur.
Definition Finding






This step tries to identify those pages that
include definitions of the search topic and its
sub-topics discovered in the previous step.
Preprocessing steps:
Texts that will not be displayed by browsers (e.g.,
<script>...</ script >,<!—comments-->) are ignored.
Word stemming is applied.
Stopwords and punctuation are kept as they serve as
clues to identify definitions.
HTML tags within a paragraph are removed.
Definition Finding

After that, following patterns are applied to
identify definitions:
[1] Bing Liu, Chee Wee Chin, Hwee Tou Ng. Mining Topic-Specific Concepts and Definitions on the Web
Definition Finding

Besides using the above patterns, the paper
also relies on HTML structuring and hyperlink
structures.

1) If a page contains only one header or one big
emphasized text segment at the beginning in the entire
document, then the document contains a definition of the
concept in the header.
2) Definitions at the second level of the hyperlink
structure are also discovered. All the patterns and
methods described above are applied to these second
level documents.

Definition Finding

Observation: Sometimes no informative page is
found for a particular sub-topic when the pages
for the main topic are very general and do not
contain detailed information for sub-topics.

In such cases, the sub-topic can be submitted to
the search engine and sub-subtopics may be
found recursively.
Dealing with Ambiguity




One of the difficult problems in concept mining is
the ambiguity of the search terms (e.g.,
classification).
A search engine may not return any page in the
right context in its top ranking pages.
Partial solution: adding terms that can represent
the context (e.g., classification data mining).
Disadvantage: returned web pages focus more
on the context words since they represent a
larger concept.
Dealing with Ambiguity

To handle this problem: First reduce the
ambiguity of a search topic by using context
words. Then,

1) Finding salient concepts only in the segment
describing the topic or sub-topic. (using HTML
structuring tags as cues).
2) Identifying those pages that hierarchically organize
knowledge of the parent topic. To identify such pages,
we can parse the HTML nested list items (e.g., <li>)
structure by building a tree.

Dealing with Ambiguity
• We confirm whether it is a correct page by finding if
the hierarchy contains at least another sub-topic of
the parent topic.
An example of a well-organized topic hierarchy
[1] Bing Liu, Chee Wee Chin, Hwee Tou Ng. Mining Topic-Specific Concepts and Definitions on the Web
Dealing with Ambiguity

Finding salient concepts enclosed within braces
illustrating examples.
Example:
There are many clustering approaches (e.g., hierarchical,
partitioning, k-means, k-medoids), and we add that efficiency is
important if the clusters contain many points.

The execution of the algorithm can stop when
most of the salient concepts found are parallel
concepts of the search topic.
Mutual Reinforcement
This method applies to situations where we have already
found the sub-topics of a topic, and we want to find the
salient concepts of the sub-topics of the topic, to go
down further.
 Often, when one searches for a sub-topic S1, one also
finds important information about another sub-topic S2
due to the ranking algorithm used by the search engine.
 This method works in two steps:
1) submit each sub-topic individually to the search engine.
2) combine the top-ranking pages from each search into
one set, and apply the proposed techniques to the whole
set to look for all sub-topics.

System Architecture

1)
2)
3)
The overall system is composed of five main
components:
A search engine: This is a standard web search
engine (Google is used in this system).
A crawler: It crawls the World Wide Web to download
those top-ranking pages returned by the search
engine. It stores the pages in “Web Page Depository”.
A salient concept miner: It uses the sub-topic
discovery techniques explained before to search the
pages stored in “Web Page Depository”, in order to
identify and extract those sub-topics and salient
concepts.
System Architecture
4) A definition finder: It uses the technique presented in
definition finding section to search through the pages
stored in “Web Page Depository” to find those
informative pages containing definitions of the topics and
the sub-topics.
5) A user interface: It enables the user to interact with the
system
System Architecture
[1] Bing Liu, Chee Wee Chin, Hwee Tou Ng. Mining Topic-Specific Concepts and Definitions on the Web
Experimental Study




The size of the set of documents is limited to the
first hundred results returned by Google.
Table 1 shows the sub-topics and salient
concepts discovered for 28 search topics
In each box, the first line gives the search topic.
For each topic, only ten top-ranking concepts
are listed.
For too specific topics, only definition finding is
meaningful.
[1] Bing Liu, Chee Wee Chin, Hwee Tou Ng. Mining Topic-Specific Concepts and Definitions on
the Web
Experimental Study

In Table 2, the precision of the definition-finding task is
compared with the Google search engine and
AskJeeves, the web’s premier question-answering
system.

The first 10 pages of results are compared with the first
10 pages returned by Google and AskJeeves. To do a
fair comparison, they also look for definitions in the
second level of the search results returned by Google
and AskJeeves.
[1] Bing Liu, Chee Wee Chin, Hwee Tou Ng. Mining Topic-Specific Concepts and Definitions on
the Web
Table 2
Experimental Study




Table 3 presents the results for ambiguity handling by
applying the respective methods explained before.
Column 1 lists two ambiguous topics of “data mining”
and “time series”. Column 2 lists the sub-topics identified
using the original technique.
Column 3 lists gives the sub-topics discovered using the
respective parent-topics as context terms.
Column 4 uses ambiguity handling techniques. Column 5
applies mutual reinforcement in addition to others.
[1] Bing Liu, Chee Wee Chin, Hwee Tou Ng. Mining Topic-Specific Concepts and Definitions on
the Web
Conclusions

The proposed techniques aim at helping
Web users to learn an unfamiliar topic indepth and systematically.

This is an efficient system to discover and
organize knowledge on the web, in a way
similar to a traditional book, to assist
learning.
Effective Personalization Based on
Association Rule
Discovery from Web Usage Data
Mikhail Bautin
Bamshad Mobasher, Honghua Dai, Tao
Luo, Miki Nakagawa
DePaul University 243 S. Wabash Ave.
Chicago, Illinois 60604, USA (2001)
References



B. Mobasher, H. Dai, T. Luo and M. Nakagawa:
"Effective Personalization Based on Association Rule
Discovery from Web Usage Data", in Proc. the 3rd ACM
Workshop on Web Information and Data Management
(WIDM01) (2001).
R. Agarwal, C. Aggarwal, and V. Prasad. A tree
projection algorithm for generation of frequent itemsets.
In Proceedings of the High Performance Data Mining
Workshop, Puerto Rico, 1999.
R. Agrawal and Ramakrishnan Srikant. Fast algorithms
for mining association rules. In Proc. 20th Int.
Conference on Very Large Data Bases, VLDB94, 1994.
Goal

Personalize a web site:
 Predict
actions of the user (pre-fetching etc.)
 Recommend new items to a customer based
on viewed items and knowledge of what other
customers are interested in:
“Customers who buy this also buy that...”
Approaches

Collaborative filtering
 Find
top k users who have similar tastes or
interests (k-nearest-neighbor)
 Predict actions based on what those users did
 Too much online computation needed

Association rules
 Scalable:
constant time query processing
 Better precision and coverage than CF
Data Preparation
Input: web server logs
 Steps:

 User
identification (trivial if using cookies)
 Session and transaction identification
 Page view identification (for multi-frame sites)

As a result of preparation:
 Records
correspond to transactions
 Items correspond to page views
 Order of page views does not matter
Pattern Discovery

Running Apriori algorithm
 Records
= transactions, items = page views
 Minimum support and confidence restriction
 Problem with global minimum support value:
important but rare items can be discarded
 Solution: multiple minimum support values.
For itemset {p1, ..., pn} require
Recommendation Engine
Fixed-size sliding window w:
subset of |w| most recent page views
 Need to find rules with w on the left
 This is done with depth-first search
 Sort elements of w lexicographically
 Only need O(|w|) to find the itemset and
O(# of page views) to produce
recommendations

Frequent Itemset Graph
Figure 1 from the paper (Mobasher et al.)
Example
Active session window w = {B, E}
 Solid lines – “lexicographic” extension
 Stippled lines – any extension
 The search leads to node BE (5) at level 3
 Possible extensions: A and C
 Confidence calculated as
 For A it is 5/5 = 1, for C it is 4/5

Window size vs minsup
For large window size it might be difficult
to find frequent enough itemsets
 But larger window gives better accuracy
 Solution: the “all-kth-order” method

 Start
with the largest possible window size
 Reduce window size until able to generate a
recommendation
 No additional computation incurred
Evaluation Methodology
For each transaction t first n page views
are used for generating recommendation
and last |t| - n are used for testing
 ast – subset of first n elements of t
  – minimum required confidence
 R(ast, ) – set of recommendations
 evalt – the last |t| - n pageviews of t

Measures of Evaluation

The threshold  is ranging from 0.1 to 1
Impact of Window Size
Figure 2 from the paper (Mobasher et al.)
Single vs Multiple Min. Support
Figure 3 from the paper (Mobasher et al.)
The all-kth-order Model
Figure 4 from the paper (Mobasher et al.)
Association Rules vs kNN
Figure 5 from the paper (Mobasher et al.)
Conclusions

Personalization based on association rules
is better than k-nearest-neighbor approach:
– very little online computation
 Therefore, better scalability
 Better precision
 Better coverage
 Faster

Effective alternative to standard
collaborative filtering mechanisms for
personalization
Thank you!
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CSE 634 Data Mining Concepts and Techniques