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Title: Course on Data Mining (581550-4)


1
Course on Data Mining (581550-4)
Intro/Ass. Rules
Clustering
Episodes
KDD Process
Text Mining
Appl./Summary
2
Course on Data Mining (581550-4)
Today 26.10.2001
  • Summary
  • Course organization
  • Summary
  • What is data mining?
  • Today's subject
  • Association rules
  • Next week's program
  • Lecture Episodes
  • Exercise Associations
  • Seminar Associations

3
Course Organization
Lectures, Exercises, Exam
  • 12 lectures 24.10.-30.11.2001
  • Wed 14-16, Fri 12-14 (A217)
  • Wed normal lecture
  • Fri seminar like lecture (except for 26.10.)
  • 5 exercise sessions 1.11.-29.11.2001
  • Thu 12-14 (A318)
  • Home exam
  • Given 28.11., Returned due 21.12.2001
  • Language
  • Lecturing language is Finnish
  • Slides and material are in English

4
Course Organization
Group Work
  • Group for seminar (and exercise) work
  • 10 groups, à 3 persons, 2 groups/lecture
  • Dates are agreed at the beginning of course
  • Articles are given on previous week's Wed
  • Seminar presentations
  • Presentation in an HTML page (around 3-5 printed
    pages) due to seminar starting
  • Can be either a HTML page or a printable document
    in PostScript/PDF format
  • 30 minutes of presentation
  • 5-15 minutes of discussion
  • Active participation

5
Course Organization / Groups
  • Group presentation time allocation
  • Fri 2.11. Group 1, Group 2 (associations)
  • Fri 9.11. Group 3, Group 4 (episodes)
  • Fri 16.11. Group 5, Group 6 (text mining)
  • Fri 23.11. Group 7, Group 8 (clustering)
  • Fri 30.11. Group 9, Group 10 (KDD process)

6
Course Organization
Course Evaluation
  • Passing the course min 30 points
  • home exam min 13 points (max 30 points)
  • exercises/experiments min 8 points (max 20
    points)
  • at least 3 returned and reported experiments
  • group presentation min 4 points (max 10 points)
  • Remember also the other requirements
  • Attending the lectures (5/7)
  • Attending the seminars (4/5)
  • Attending the exercises (4/5)

7
Course Organization
Course Material
  • Lecture slides
  • Original articles
  • Seminar presentations
  • Book "Data Mining Concepts and Techniques" by
    Jiawei Han and Micheline Kamber, Morgan Kaufmann
    Publishers, August 2000. 550 pages. ISBN
    1-55860-489-8
  • Remember to check course website and folder for
    the material!

8
SummaryWhat is Data Mining?
  • Ultimately
  • "Extraction of interesting (non-trivial,
    implicit, previously unknown, potentially useful)
    information or patterns from data in large
    databases"
  • Often just
  • "Tell something interesting about this data",
    "Describe this data"
  • Exploratory, semi-automatic data analysis on
    large data sets

9
SummaryWhat is Data Mining?
  • Data mining semi-automatic discovery of
    interesting patterns from large data sets
  • Knowledge discovery is a process
  • Preprocessing
  • Data mining
  • Postprocessing
  • To be mined, used or utilized different
  • Databases (relational, object-oriented, spatial,
    WWW, )
  • Knowledge (characterization, clustering,
    association, )
  • Techniques (machine learning, statistics,
    visualization, )
  • Applications (retail, telecom, Web mining, log
    analysis, )

10
Summary Typical KDD Process
Operational Database
Data mining
Input data
Results
2
Utilization
11
Association Rules
Basics
Examples
Generation
Multi-level Rules
Constraints
12
Market Basket Analysis
  • Analysis of customer buying habits by finding
    associations and correlations between the
    different items that customers place in their
    "shopping basket"

13
Market Basket Analysis
  • Given
  • A database of customer transactions (e.g.,
    shopping baskets), where each transaction is a
    set of items (e.g., products)
  • Find
  • Groups of items which are frequently purchased
    together

14
Market Basket Analysis
  • Extract information on purchasing behavior
  • "IF buys beer and sausage, THEN also by mustard
    with high probability"
  • Actionable information can suggest...
  • New store layouts and product assortments
  • Which products to put on promotion
  • MBA approach is applicable whenever a customer
    purchases multiple things in proximity
  • Credit cards
  • Services of telecommunication companies
  • Banking services
  • Medical treatments

15
Market Basket Analysis
  • Useful
  • "On Thursdays, grocery store consumers often
    purchase diapers and beer together."
  • Trivial
  • "Customers who purchase maintenance agreements
    are very likely to purchase large appliances."
  • Unexplicable/unexpected
  • "When a new hardaware store opens, one of the
    most sold items is toilet rings."

16
Association Rules Basics
  • Association rule mining
  • Finding frequent patterns, associations,
    correlations, or causal structures among sets of
    items or objects in transaction databases,
    relational databases, and other information
    repositories.
  • Comprehensibility Simple to understand
  • Utilizability Provide actionable information
  • Efficiency Efficient discovery algorithms exist
  • Applications
  • Market basket data analysis, cross-marketing,
    catalog design, loss-leader analysis, clustering,
    classification, etc.

17
Association Rules Basics
  • Typical representation formats for association
    rules
  • diapers ? beer 0.5, 60
  • buysdiapers ? buysbeer 0.5, 60
  • "IF buys diapers, THEN buys beer in 60 of the
    cases. Diapers and beer are bought together in
    0.5 of the rows in the database."
  • Other representations (used in Han's book)
  • buys(x, "diapers") ? buys(x, "beer") 0.5, 60
  • major(x, "CS") takes(x, "DB") ? grade(x, "A")
    1, 75

18
Association Rules Basics
  • diapers ? beer 0.5, 60

"IF buys diapers, THEN buys beer in 60 of the
cases in 0.5 of the rows"
  • Antecedent, left-hand side (LHS), body
  • Consequent, right-hand side (RHS), head
  • Support, frequency ("in how big part of the data
    the things in left- and right-hand sides occur
    together")
  • Confidence, strength ("if the left-hand side
    occurs, how likely the right-hand side occurs")

19
Association Rules Basics
  • Support denotes the frequency of the rule
    within transactions.
  • support(A ? B s, c ) p(A?B) support
    (A,B)
  • Confidence denotes the percentage of
    transactions containing A which contain also B.
  • confidence(A ? B s, c ) p(BA) p(A?B) /
    p(A) support(A,B) / support(A)

20
Association Rules Basics
  • Minimum support ?
  • High ? few frequent itemsets
  • ? few valid rules which occur very often
  • Low ? many valid rules which occur rarely
  • Minimum confidence ?
  • High ? few rules, but all "almost logically
    true"
  • Low ? many rules, many of them very "uncertain"
  • Typical values ? 2 -10 , ? 70 - 90

21
Association Rules Basics
  • Transaction
  • Relational format Compact format
  • ltTid,itemgt ltTid,itemsetgt
  • lt1, item1gt lt1, item1,item2gt
  • lt1, item2gt lt2, item3gt
  • lt2, item3gt
  • Item vs. itemsets single element vs. set of
    items
  • Support of an itemset I of transaction
    containing I
  • Minimum support ? threshold for support
  • Frequent itemset with support ? ?.

22
Association Rules Basics
  • Given (1) database of transactions, (2) each
    transaction is a list of items bought (purchased
    by a customer in a visit)
  • Find all rules with minimum support and
    confidence
  • If min. support 50 and min. confidence 50, then
  • A ? C 50, 66.6, C ? A 50, 100

23
Association Rule Generation
  • Association rule mining is a two-step process
  • STEP 1 Find the frequent itemsets the sets of
    items that have minimum support.
  • So called Apriori trick A subset of a frequent
    itemset must also be a frequent itemset
  • i.e., if AB is a frequent itemset, both A and
    B should be frequent itemsets
  • Iteratively find frequent itemsets with size from
    1 to k (k-itemset)
  • STEP 2 Use the frequent itemsets to generate
    association rules.

24
Frequent Sets with Apriori
  • Join Step Ck is generated by joining Lk-1with
    itself
  • Prune Step Any (k-1)-itemset that is not
    frequent cannot be a subset of a frequent
    k-itemset
  • Pseudo-code
  • Ck Candidate itemset of size k Lk Frequent
    itemset of size k
  • L1 frequent items
  • for (k 1 Lk !? k) do begin
  • Ck1 candidates generated from Lk
  • for each transaction t in database do
  • increment the count of all candidates in
    Ck1 that are
    contained in t
  • Lk1 candidates in Ck1 with
    min_support
  • end
  • return ?k Lk

25
Apriori Candidate Generation
  • The Apriori principle
  • Any subset of a frequent itemset must be frequent
  • L3abc, abd, acd, ace, bcd
  • Self-joining L3L3
  • abcd from abc and abd
  • acde from acd and ace
  • Pruning
  • acde is removed because ade is not in L3
  • C4abcd

26
Apriori Example (1/6)
27
Apriori Example (2/6)
28
Apriori Example (3/6)
29
Apriori Example (4/6)
Search Space of Database D
12345
1234 1235 1245 1345 2345
123 124 125 134 135 145 234
235 245 345
12 13 14 15 23 24 25
34 35 45
1 2 3 4 5
30
Apriori Example (5/6)
Apriori Trick on Level 1
12345
1234 1235 1245 1345 2345
123 124 125 134 135 145 234
235 245 345
12 13 14 15 23 24 25
34 35 45
1 2 3 4 5
31
Apriori Example (6/6)
Apriori Trick on Level 2
12345
1234 1235 1245 1345 2345
123 124 125 134 135 145 234
235 245 345
12 13 14 15 23 24 25
34 35 45
1 2 3 4 5
32
Is Apriori Fast Enough?
  • The core of the Apriori algorithm
  • Use frequent (k 1)-itemsets to generate
    candidate frequent k-itemsets
  • Use database scan and pattern matching to collect
    counts for the candidate itemsets
  • The bottleneck of Apriori candidate generation
  • Huge candidate sets
  • 104 frequent 1-itemset will generate 107
    candidate 2-itemsets
  • To discover a frequent pattern of size 100, e.g.,
    a1, a2, , a100, one needs to generate 2100 ?
    1030 candidates.
  • Multiple scans of database
  • Needs (n 1 ) scans, n is the length of the
    longest pattern

33
Is Apriori Fast Enough?
  • In practice
  • For basic Apriori approach the number of
    attributes in the row is usually much more
    critical than the number of transaction rows
  • For example
  • 50 attributes each having 1-3 values, 100.000
    rows (not very bad)
  • 50 attributes each having 10-100 values, 100.000
    rows (quite bad)
  • 10.000 attributes each having 5-10 values, 100
    rows (very bad...)
  • Notice
  • One attribute might have several different values
  • Association rule algorithms typically treat every
    attribute-value pair as one attribute (2
    attribute with 5 values each gt "10 attributes")
  • There are some ways to overcome the problem...

34
Improving Apriori Performance
  • Hash-based itemset counting
  • A k-itemset whose corresponding hashing bucket
    count is below the threshold cannot be frequent
  • Transaction reduction
  • A transaction that does not contain any frequent
    k-itemset is useless in subsequent scans
  • Partitioning
  • Any itemset that is potentially frequent in DB
    must be frequent in at least one of the
    partitions of DB
  • Sampling
  • Mining on a subset of given data, lower support
    threshold a method to determine the completeness

35
Association Rules from Itemsets
  • Pseudo-code
  • for every frequent itemset l
  • generate all nonempty subsets s of l
  • for every nonempty subset s of l
  • output the rule "s ? (l-s)" if
    support(l)/support(s) ? min_conf", where
    min_conf is the minimum confidence threshold
  • E.g. frequent set l abc, subsets s a, b,
    c, ab, ac, bc)
  • a ? b, a ? c, b ? c
  • a ? bc, b ? ac, c ? ab
  • ab ? c, ac ? b, bc ? a

36
Association Rule Generation
  • Rule 1 to remember
  • Generating frequent sets is slow (especially
    itemsets of size 2)
  • Generating association rules from frequent
    itemsets is fast
  • Rule 2 to remember
  • For itemset generation, support threshold is used
  • For association rules, confidence threshold is
    used
  • What happens in reality, how long does it take to
    create frequent sets and association rules?
  • Let's take small real-life examples
  • Experiments are made with Citum 4/275 Alpha
    server with 512 MB of main memory Red Hat Linux
    release 5.0 (kernel 2.0.30)

37
Performance Example (1/4)
Alarms
38
Performance Example (2/4)
  • Telecom data containing alarms
  • 1234 EL1 PCM 940926082623 A1 ALARMTEXT..
  • Example data 1
  • 43 478 alarms (26.9.94 - 5.10.94 10 days)
  • 2 234 different types of alarms, 23 attributes,
    5503 different values
  • Example data 2
  • 73 679 alarms (1.2.95 - 22.3.95 7 weeks)
  • 287 different types of alarms, 19 attributes,
    3411 different values

Alarm type
Date, time
Alarm severity class
Alarming network element
Alarm number
39
Performance Example (3/4)
Example rule alarm_number1234, alarm_typePCM
? alarm_severityA1 2,45
40
Performance Example (4/4)
  • Example results for data 1
  • Frequency threshold 0.1 (lowest
    possible with this data)
  • Candidate sets 109 719 Time 12.02 s
  • Frequent sets 79 311 Time 64 855.73 s
  • Rules 3 750 000 Time 860.60 s
  • Example results for data 2
  • Frequency threshold 0.1 (lowest
    possible with this data)
  • Candidate sets 43 600 Time 1.70 s
  • Frequent sets 13 321 Time 10 478.93 s
  • Rules 509 075 Time 143.35 s

41
Selecting the Interesting Rules?
  • Usually the result set is very big, one must
    select interesting ones based on
  • Objective measures
  • Two popular measurements
  • support and
  • confidence
  • Subjective measures (Silberschatz Tuzhilin,
    KDD95)
  • A rule (pattern) is interesting if
  • it is unexpected (surprising to the user)
    and/or
  • actionable (the user can do something with it)
  • These issues will be discussed with KDD processes

42
Boolean vs. Quantitative Rules
  • Boolean vs. quantitative association rules (based
    on the types of values handled)
  • Boolean Rule concerns associations between the
    presence or absence of items (e.g. "buys A" or
    "does not buy A")
  • buysSQLServer, buysDMBook ? buysDBMiner
    2,60
  • buys(x, "SQLServer") buys(x, "DMBook")
    buys(x, "DBMiner") 0.2, 60
  • Quantitative Rule concerns associations between
    quantitative items or attributes
  • age30..39, income42..48K ? buysPC 1, 75
  • age(x, "30..39") income(x, "42..48K")
    buys(x, "PC") 1, 75

43
Quantitative Rules
  • Quantitative attributes e.g., age, income,
    height, weight
  • Categorical attributes e.g., color of car

Problem too many distinct values for
quantitative attributes Solution transform
quantitative attributes in categorical ones via
discretization ? more about this in seminar!
44
Single- vs. Multi-dimensional Rules
  • Single-dimensional vs. multi-dimensional
    associations
  • Single-dimensional Items or attributes in the
    rule refer to only one dimension (e.g., to
    "buys")
  • Beer, Chips ? Bread 0.4, 52
  • buys(x, "Beer") buys(x, "Chips") buys(x,
    "Bread") 0.4, 52
  • Multi-dimensional Items or attributes in the
    rule refer to two or more dimensions (e.g.,
    "buys", "time_of_transaction", "customer_category"
    )
  • In the following example nationality, age,
    income

45
Multi-dimensional Rules
RULES nationality French ? income high 50,
100 income high ? nationality French 50,
75 age 50 ? nationality Italian 33, 100
46
Single- vs. Multi-level Rules
  • Single-level vs. multi-level associations
  • Single-level Associations between items or
    attributes from the same level of abstraction
    (i.e., from the same level of hierarchy)
  • Beer, Chips ? Bread 0.4, 52
  • Multi-level Associations between items or
    attributes from different levels of abstraction
    (i.e, from different levels of hierarchy)
  • BeerKarjala, ChipsEstrellaBarbeque ? Bread
    0.1, 74
  • More about multi-level association rules on the
    next slides

47
Multi-level Association Rules
  • Is difficult to find interesting patterns at a
    too primitive level
  • high support too few rules
  • low support too many rules, most uninteresting
  • Approach reason at suitable level of abstraction
  • A common form of background knowledge is that an
    attribute may be generalized or specialized
    according to a hierarchy of concepts
  • Multi-level association rules rules which
    combine associations with hierarchy of concepts

48
Multi-level Association Rules
  • Items often form hierarchies
  • Items at the lower level are expected to have
    lower support
  • Rules regarding itemsets at appropriate levels
    could be quite useful
  • Transaction database can be encoded based on
    dimensions and levels

49
Multi-level Association Rules
1
2
1
2
1
2
1
2
121 milk - 2 - Fraser
50
Multi-level Association Rules
  • A top-down, progressive deepening approach
  • First find high-level strong rules
  • milk bread 20,
    60
  • Then find their lower-level "weaker" rules
  • 2 milk wheat bread
    6, 50
  • Variations at mining multi-level association
    rules
  • Level-crossed association rules
  • milk wheat bread
  • Association rules with multiple, alternative
    hierarchies
  • milk Wonder bread

51
Multi-level Association Rules
  • Generalizing/specializing values of attributes
  • ...from specialized to general support of rules
    increases (new rules may become valid)
  • ...from general to specialized support of rules
    decreases (rules may become not valid, their
    support falls under the threshold)
  • Too low level gt too many rules and too primitive
  • Pepsi light 0.5l bottle ? Taffel Barbeque Chips
    200gr
  • Too high level gt uninteresting rules
  • Food ? Clothes

52
Redundancy Filtering
  • Some rules may be redundant due to "ancestor"
    relationships between items
  • Example (milk has 4 subclasses)
  • milk ? wheat bread support 8, confidence
    70
  • 2 milk ? wheat bread support 2, confidence
    72
  • We say the first rule is an ancestor of the
    second rule
  • A rule is redundant if its support is close to
    the "expected" value, based on the rules
    ancestor
  • Above the second rule could be redundant

53
Uniform vs. Reduced Support
  • Uniform Support the same minimum support for all
    levels
  • One minimum support threshold. No need to
    examine itemsets containing any item whose
    ancestors do not have minimum support.
  • Lower level items do not occur as frequently.
    If support threshold
  • too high ? miss low level associations
  • too low ? generate too many high level
    associations
  • Reduced Support reduced minimum support at lower
    levels

54
Uniform Support
Multi-level mining with uniform support
Level 1 min_sup 5
Milk support 10
2 Milk support 6
Skim Milk support 4
Level 2 min_sup 5
55
Reduced Support
Multi-level mining with reduced support
Level 1 min_sup 5
Milk support 10
2 Milk support 6
Skim Milk support 4
Level 2 min_sup 3
56
Progressive Deepening
  • A top-down, progressive deepening approach
  • First mine high-level frequent items
  • milk (15), bread
    (10)
  • Then mine their lower-level "weaker" frequent
    itemsets
  • 2 milk (5),
    wheat bread (4)
  • Different min_support thresholds across
    multi-levels lead to different algorithms
  • If adopting the same min_support across
    multi-levels
  • then do not examine t if any of ts ancestors is
    infrequent
  • If adopting reduced min_support at lower levels
  • then examine only those descendents whose
    ancestors support is frequent/non-negligible

57
Constraint-Based Mining
  • Interactive, exploratory mining giga-bytes of
    data?
  • Could it be real? By making good use of
    constraints!
  • What kinds of constraints can be used in mining?
  • Knowledge type constraint classification,
    association, etc.
  • Data constraint SQL-like queries
  • Find product pairs sold together in Vancouver in
    Dec.98
  • Dimension/level constraints
  • In relevance to region, price, brand, customer
    category
  • Interestingness constraints
  • Strong rules (min_support ? 3, min_confidence ?
    60)
  • Rule constraints (see the next slides)

58
Rule Constraints
  • Two kinds of rule constraints
  • Rule form constraints meta-rule guided mining
  • Metarule P(X, Y) Q(X, W) takes(X,
    "database systems")
  • Matching rule age(X, "30..39") income(X,
    "41K..60K") takes(X, "database systems").
  • Rule content constraint constraint-based query
    optimization (Ng, et al., SIGMOD98)
  • sum(LHS) lt 100 min(LHS) gt 20 count(LHS) gt 3
    sum(RHS) gt 1000

59
Rule Constraints
  • 1-variable vs. 2-variable constraints
    (Lakshmanan, et al. SIGMOD99)
  • 1-var A constraint confining only one side (L/R)
    of the rule, e.g.,
  • sum(LHS) lt 100 min(LHS) gt 20 count(LHS) gt 3
    sum(RHS) gt 1000
  • 2-var A constraint confining both sides (L and
    R).
  • sum(LHS) lt min(RHS) max(RHS) lt 5 sum(LHS)

60
Summary
  • Association rule mining
  • Probably the most significant contribution from
    the database community in KDD
  • Rather simple concept, but the "thinking" gives
    basis for extensions and other methods
  • A large number of papers have been published
  • Many interesting issues have been explored
  • Interesting research directions
  • Association analysis in other types of data
    spatial data, multimedia data, time series data,
    etc.

61
References (1/5)
  • R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A
    tree projection algorithm for generation of
    frequent itemsets. In Journal of Parallel and
    Distributed Computing (Special Issue on High
    Performance Data Mining), 2000.
  • R. Agrawal, T. Imielinski, and A. Swami. Mining
    association rules between sets of items in large
    databases. SIGMOD'93, 207-216, Washington, D.C.
  • R. Agrawal and R. Srikant. Fast algorithms for
    mining association rules. VLDB'94 487-499,
    Santiago, Chile.
  • R. Agrawal and R. Srikant. Mining sequential
    patterns. ICDE'95, 3-14, Taipei, Taiwan.
  • R. J. Bayardo. Efficiently mining long patterns
    from databases. SIGMOD'98, 85-93, Seattle,
    Washington.
  • S. Brin, R. Motwani, and C. Silverstein. Beyond
    market basket Generalizing association rules to
    correlations. SIGMOD'97, 265-276, Tucson,
    Arizona.
  • S. Brin, R. Motwani, J. D. Ullman, and S. Tsur.
    Dynamic itemset counting and implication rules
    for market basket analysis. SIGMOD'97, 255-264,
    Tucson, Arizona, May 1997.
  • K. Beyer and R. Ramakrishnan. Bottom-up
    computation of sparse and iceberg cubes.
    SIGMOD'99, 359-370, Philadelphia, PA, June 1999.
  • D.W. Cheung, J. Han, V. Ng, and C.Y. Wong.
    Maintenance of discovered association rules in
    large databases An incremental updating
    technique. ICDE'96, 106-114, New Orleans, LA.
  • M. Fang, N. Shivakumar, H. Garcia-Molina, R.
    Motwani, and J. D. Ullman. Computing iceberg
    queries efficiently. VLDB'98, 299-310, New York,
    NY, Aug. 1998.

62
References (2/5)
  • G. Grahne, L. Lakshmanan, and X. Wang. Efficient
    mining of constrained correlated sets. ICDE'00,
    512-521, San Diego, CA, Feb. 2000.
  • Y. Fu and J. Han. Meta-rule-guided mining of
    association rules in relational databases.
    KDOOD'95, 39-46, Singapore, Dec. 1995.
  • T. Fukuda, Y. Morimoto, S. Morishita, and T.
    Tokuyama. Data mining using two-dimensional
    optimized association rules Scheme, algorithms,
    and visualization. SIGMOD'96, 13-23, Montreal,
    Canada.
  • E.-H. Han, G. Karypis, and V. Kumar. Scalable
    parallel data mining for association rules.
    SIGMOD'97, 277-288, Tucson, Arizona.
  • J. Han, G. Dong, and Y. Yin. Efficient mining of
    partial periodic patterns in time series
    database. ICDE'99, Sydney, Australia.
  • J. Han and Y. Fu. Discovery of multiple-level
    association rules from large databases. VLDB'95,
    420-431, Zurich, Switzerland.
  • J. Han, J. Pei, and Y. Yin. Mining frequent
    patterns without candidate generation. SIGMOD'00,
    1-12, Dallas, TX, May 2000.
  • T. Imielinski and H. Mannila. A database
    perspective on knowledge discovery.
    Communications of ACM, 3958-64, 1996.
  • M. Kamber, J. Han, and J. Y. Chiang.
    Metarule-guided mining of multi-dimensional
    association rules using data cubes. KDD'97,
    207-210, Newport Beach, California.
  • M. Klemettinen, H. Mannila, P. Ronkainen, H.
    Toivonen, and A.I. Verkamo. Finding interesting
    rules from large sets of discovered association
    rules. CIKM'94, 401-408, Gaithersburg, Maryland.

63
References (3/5)
  • F. Korn, A. Labrinidis, Y. Kotidis, and C.
    Faloutsos. Ratio rules A new paradigm for fast,
    quantifiable data mining. VLDB'98, 582-593, New
    York, NY.
  • B. Lent, A. Swami, and J. Widom. Clustering
    association rules. ICDE'97, 220-231, Birmingham,
    England.
  • H. Lu, J. Han, and L. Feng. Stock movement and
    n-dimensional inter-transaction association
    rules. SIGMOD Workshop on Research Issues on
    Data Mining and Knowledge Discovery (DMKD'98),
    121-127, Seattle, Washington.
  • H. Mannila, H. Toivonen, and A. I. Verkamo.
    Efficient algorithms for discovering association
    rules. KDD'94, 181-192, Seattle, WA, July 1994.
  • H. Mannila, H Toivonen, and A. I. Verkamo.
    Discovery of frequent episodes in event
    sequences. Data Mining and Knowledge Discovery,
    1259-289, 1997.
  • R. Meo, G. Psaila, and S. Ceri. A new SQL-like
    operator for mining association rules. VLDB'96,
    122-133, Bombay, India.
  • R.J. Miller and Y. Yang. Association rules over
    interval data. SIGMOD'97, 452-461, Tucson,
    Arizona.
  • R. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang.
    Exploratory mining and pruning optimizations of
    constrained associations rules. SIGMOD'98, 13-24,
    Seattle, Washington.
  • N. Pasquier, Y. Bastide, R. Taouil, and L.
    Lakhal. Discovering frequent closed itemsets for
    association rules. ICDT'99, 398-416, Jerusalem,
    Israel, Jan. 1999.

64
References (4/5)
  • J.S. Park, M.S. Chen, and P.S. Yu. An effective
    hash-based algorithm for mining association
    rules. SIGMOD'95, 175-186, San Jose, CA, May
    1995.
  • J. Pei, J. Han, and R. Mao. CLOSET An Efficient
    Algorithm for Mining Frequent Closed Itemsets.
    DMKD'00, Dallas, TX, 11-20, May 2000.
  • J. Pei and J. Han. Can We Push More Constraints
    into Frequent Pattern Mining? KDD'00. Boston,
    MA. Aug. 2000.
  • G. Piatetsky-Shapiro. Discovery, analysis, and
    presentation of strong rules. In G.
    Piatetsky-Shapiro and W. J. Frawley, editors,
    Knowledge Discovery in Databases, 229-238.
    AAAI/MIT Press, 1991.
  • B. Ozden, S. Ramaswamy, and A. Silberschatz.
    Cyclic association rules. ICDE'98, 412-421,
    Orlando, FL.
  • J.S. Park, M.S. Chen, and P.S. Yu. An effective
    hash-based algorithm for mining association
    rules. SIGMOD'95, 175-186, San Jose, CA.
  • S. Ramaswamy, S. Mahajan, and A. Silberschatz. On
    the discovery of interesting patterns in
    association rules. VLDB'98, 368-379, New York,
    NY..
  • S. Sarawagi, S. Thomas, and R. Agrawal.
    Integrating association rule mining with
    relational database systems Alternatives and
    implications. SIGMOD'98, 343-354, Seattle, WA.
  • A. Savasere, E. Omiecinski, and S. Navathe. An
    efficient algorithm for mining association rules
    in large databases. VLDB'95, 432-443, Zurich,
    Switzerland.
  • A. Savasere, E. Omiecinski, and S. Navathe.
    Mining for strong negative associations in a
    large database of customer transactions. ICDE'98,
    494-502, Orlando, FL, Feb. 1998.

65
References (5/5)
  • C. Silverstein, S. Brin, R. Motwani, and J.
    Ullman. Scalable techniques for mining causal
    structures. VLDB'98, 594-605, New York, NY.
  • R. Srikant and R. Agrawal. Mining generalized
    association rules. VLDB'95, 407-419, Zurich,
    Switzerland, Sept. 1995.
  • R. Srikant and R. Agrawal. Mining quantitative
    association rules in large relational tables.
    SIGMOD'96, 1-12, Montreal, Canada.
  • R. Srikant, Q. Vu, and R. Agrawal. Mining
    association rules with item constraints. KDD'97,
    67-73, Newport Beach, California.
  • H. Toivonen. Sampling large databases for
    association rules. VLDB'96, 134-145, Bombay,
    India, Sept. 1996.
  • D. Tsur, J. D. Ullman, S. Abitboul, C. Clifton,
    R. Motwani, and S. Nestorov. Query flocks A
    generalization of association-rule mining.
    SIGMOD'98, 1-12, Seattle, Washington.
  • K. Yoda, T. Fukuda, Y. Morimoto, S. Morishita,
    and T. Tokuyama. Computing optimized rectilinear
    regions for association rules. KDD'97, 96-103,
    Newport Beach, CA, Aug. 1997.
  • M. J. Zaki, S. Parthasarathy, M. Ogihara, and W.
    Li. Parallel algorithm for discovery of
    association rules. Data Mining and Knowledge
    Discovery, 1343-374, 1997.
  • M. Zaki. Generating Non-Redundant Association
    Rules. KDD'00. Boston, MA. Aug. 2000.
  • O. R. Zaiane, J. Han, and H. Zhu. Mining
    Recurrent Items in Multimedia with Progressive
    Resolution Refinement. ICDE'00, 461-470, San
    Diego, CA, Feb. 2000.

66
Course Organization
Next Week
  • Lecture 31.10. Episodes and recurrent patterns
  • Mika gives the lecture
  • Excercise 1.11. Associations
  • Pirjo takes care of you! -)
  • Seminar 2.11. Associations
  • Pirjo gives the lecture
  • 2 group presentations

67
Seminar Presentations
  • Seminar presentations
  • Articles are given on previous week's Wed
  • Presentation in an HTML page (around 3-5 printed
    pages) due to seminar starting
  • Can be either a HTML page or a printable document
    in PostScript/PDF format
  • 30 minutes of presentation
  • 5-15 minutes of discussion
  • Active participation

68
Seminar Presentations
  • Seminar presentations
  • Try to understand the "message" in the article
  • Try to present the basic ideas as clearly as
    possible, use examples
  • Do not present detailed mathematics or algorithms
  • Test do you understand your own presentation?
  • In the presentation, use PowerPoint or
    conventional slides

69
Seminar Presentations/Groups 1-2
Quantitative Rules
R. Srikant, R. Agrawal "Mining Quantitative
Association Rules in Large Relational Tables",
Proc. of the ACM-SIGMOD 1996
MINERULE
Rosa Meo, Giuseppe Psaila, Stefano Ceri "A New
SQL-like Operator for Mining Association Rules".
VLDB 1996 122-133
70
Introduction to Data Mining (DM)
Thank you for your attention and have a nice
weekend! Thanks to Jiawei Han from Simon Fraser
University for his slides which greatly helped
in preparing this lecture! Also thanks to Fosca
Giannotti and Dino Pedreschi from Pisa for their
slides.
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