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CPS216: Advanced Database Systems Data Mining. Slides created by Jeffrey Ullman, Stanford. What is Data Mining?. Discovery of useful, possibly unexpected, patterns in data. Subsidiary issues: Data cleansing : detection of bogus data. E.g., age = 150.

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cps216 advanced database systems data mining

CPS216: Advanced Database SystemsData Mining

Slides created by Jeffrey Ullman, Stanford

what is data mining
What is Data Mining?
  • Discovery of useful, possibly unexpected, patterns in data.
  • Subsidiary issues:
    • Data cleansing: detection of bogus data.
      • E.g., age = 150.
    • Visualization: something better than megabyte files of output.
    • Warehousing of data (for retrieval).
typical kinds of patterns
Typical Kinds of Patterns
  • Decision trees: succinct ways to classify by testing properties.
  • Clusters: another succinct classification by similarity of properties.
  • Bayes, hidden-Markov, and other statistical models, frequent-itemsets: expose important associations within data.
example clusters
Example: Clusters

x

xx x

x x

x x x

x

x x x

x

x

x x

x x x x

x x x x

x x x

x x

x x

x x x x

x x x

x

x

example frequent itemsets
Example: Frequent Itemsets
  • A common marketing problem: examine what people buy together to discover patterns.
    • What pairs of items are unusually often found together at Kroger checkout?
      • Answer: diapers and beer.
    • What books are likely to be bought by the same Amazon customer?
meaningfulness of answers
Meaningfulness of Answers
  • A big risk when data mining is that you will “discover” patterns that are meaningless.
  • Statisticians call it Bonferroni’s principle: (roughly) if you look in more places for interesting patterns than your amount of data will support, you are bound to find “false patterns”.
rhine paradox 1
Rhine Paradox --- (1)
  • David Rhine was a parapsychologist in the 1950’s who hypothesized that some people had Extra-Sensory Perception.
  • He devised an experiment where subjects were asked to guess 10 hidden cards --- red or blue.
  • He discovered that almost 1 in 1000 had ESP --- they were able to get all 10 right!
rhine paradox 2
Rhine Paradox --- (2)
  • He told these people they had ESP and called them in for another test of the same type.
  • Alas, he discovered that almost all of them had lost their ESP.
  • What did he conclude?
    • Answer on next slide.
rhine paradox 3
Rhine Paradox --- (3)
  • He concluded that you shouldn’t tell people they have ESP; it causes them to lose it.
association rules

“Association Rules”

Market Baskets

Frequent Itemsets

A-priori Algorithm

the market basket model
The Market-Basket Model
  • A large set of items, e.g., things sold in a supermarket.
  • A large set of baskets, each of which is a small set of the items, e.g., the things one customer buys on one day.
association rule mining
Association Rule Mining

transaction

id

products

bought

customer

id

sales

records:

market-basket

data

  • Trend: Products p5, p8 often bought together
support
Support
  • Simplest question: find sets of items that appear “frequently” in the baskets.
  • Support for itemset I = the number of baskets containing all items in I.
  • Given a support threshold s, sets of items that appear in >s baskets are called frequent itemsets.
example
Example
  • Items={milk, coke, pepsi, beer, juice}.
  • Support = 3 baskets.

B1 = {m, c, b} B2 = {m, p, j}

B3 = {m, b} B4 = {c, j}

B5 = {m, p, b} B6 = {m, c, b, j}

B7 = {c, b, j} B8 = {b, c}

  • Frequent itemsets: {m}, {c}, {b}, {j}, {m, b}, {c, b}, {j, c}.
applications 1
Applications --- (1)
  • Real market baskets: chain stores keep terabytes of information about what customers buy together.
    • Tells how typical customers navigate stores, lets them position tempting items.
    • Suggests tie-in “tricks,” e.g., run sale on diapers and raise the price of beer.
  • High support needed, or no $$’s .
applications 2
Applications --- (2)
  • “Baskets” = documents; “items” = words in those documents.
    • Lets us find words that appear together unusually frequently, i.e., linked concepts.
  • “Baskets” = sentences, “items” = documents containing those sentences.
    • Items that appear together too often could represent plagiarism.
applications 3
Applications --- (3)
  • “Baskets” = Web pages; “items” = linked pages.
    • Pairs of pages with many common references may be about the same topic.
  • “Baskets” = Web pages p ; “items” = pages that link to p .
    • Pages with many of the same links may be mirrors or about the same topic.
important point
Important Point
  • “Market Baskets” is an abstraction that models any many-many relationship between two concepts: “items” and “baskets.”
    • Items need not be “contained” in baskets.
  • The only difference is that we count co-occurrences of items related to a basket, not vice-versa.
scale of problem
Scale of Problem
  • WalMart sells 100,000 items and can store billions of baskets.
  • The Web has over 100,000,000 words and billions of pages.
association rules1
Association Rules
  • If-then rules about the contents of baskets.
  • {i1, i2,…,ik} →jmeans: “if a basket contains all of i1,…,ik then it is likely to contain j.
  • Confidence of this association rule is the probability of j given i1,…,ik.
example1
Example

+

_

_

+

B1 = {m, c, b} B2 = {m, p, j}

B3 = {m, b} B4 = {c, j}

B5 = {m, p, b} B6 = {m, c, b, j}

B7 = {c, b, j} B8 = {b, c}

  • An association rule: {m, b} →c.
    • Confidence = 2/4 = 50%.
interest
Interest
  • The interest of an association rule is the absolute value of the amount by which the confidence differs from what you would expect, were items selected independently of one another.
example2
Example

B1 = {m, c, b} B2 = {m, p, j}

B3 = {m, b} B4 = {c, j}

B5 = {m, p, b} B6 = {m, c, b, j}

B7 = {c, b, j} B8 = {b, c}

  • For association rule {m, b} →c, item c appears in 5/8 of the baskets.
  • Interest = | 2/4 - 5/8 | = 1/8 --- not very interesting.
relationships among measures
Relationships Among Measures
  • Rules with high support and confidence may be useful even if they are not “interesting.”
    • We don’t care if buying bread causes people to buy milk, or whether simply a lot of people buy both bread and milk.
  • But high interest suggests a cause that might be worth investigating.
finding association rules
Finding Association Rules
  • A typical question: “find all association rules with support ≥s and confidence ≥c.”
    • Note: “support” of an association rule is the support of the set of items it mentions.
  • Hard part: finding the high-support (frequent ) itemsets.
    • Checking the confidence of association rules involving those sets is relatively easy.
computation model
Computation Model
  • Typically, data is kept in a “flat file” rather than a database system.
    • Stored on disk.
    • Stored basket-by-basket.
    • Expand baskets into pairs, triples, etc. as you read baskets.
computation model 2
Computation Model --- (2)
  • The true cost of mining disk-resident data is usually the number of disk I/O’s.
  • In practice, association-rule algorithms read the data in passes --- all baskets read in turn.
  • Thus, we measure the cost by the number of passes an algorithm takes.
main memory bottleneck
Main-Memory Bottleneck
  • In many algorithms to find frequent itemsets we need to worry about how main memory is used.
    • As we read baskets, we need to count something, e.g., occurrences of pairs.
    • The number of different things we can count is limited by main memory.
    • Swapping counts in/out is a disaster.
finding frequent pairs
Finding Frequent Pairs
  • The hardest problem often turns out to be finding the frequent pairs.
  • We’ll concentrate on how to do that, then discuss extensions to finding frequent triples, etc.
na ve algorithm
Naïve Algorithm
  • A simple way to find frequent pairs is:
    • Read file once, counting in main memory the occurrences of each pair.
      • Expand each basket of n items into its n (n -1)/2 pairs.
  • Fails if #items-squared exceeds main memory.
details of main memory counting
Details of Main-Memory Counting
  • There are two basic approaches:
    • Count all item pairs, using a triangular matrix.
    • Keep a table of triples [i, j, c] = the count of the pair of items {i,j } is c.
  • (1) requires only (say) 4 bytes/pair; (2) requires 12 bytes, but only for those pairs with >0 counts.
slide32

4 per pair

12 per

occurring pair

Method (1)

Method (2)

details of approach 1
Details of Approach (1)
  • Number items 1,2,…
  • Keep pairs in the order {1,2}, {1,3},…, {1,n }, {2,3}, {2,4},…,{2,n }, {3,4},…, {3,n },…{n -1,n }.
  • Find pair {i, j } at the position (i –1)(n –i /2) + j – i.
  • Total number of pairs n (n –1)/2; total bytes about 2n 2.
details of approach 2
Details of Approach (2)
  • You need a hash table, with i and j as the key, to locate (i, j, c) triples efficiently.
    • Typically, the cost of the hash structure can be neglected.
  • Total bytes used is about 12p, where p is the number of pairs that actually occur.
    • Beats triangular matrix if at most 1/3 of possible pairs actually occur.
a priori algorithm 1
A-Priori Algorithm --- (1)
  • A two-pass approach called a-priori limits the need for main memory.
  • Key idea: monotonicity : if a set of items appears at least s times, so does every subset.
    • Contrapositive for pairs: if item i does not appear in s baskets, then no pair including i can appear in s baskets.
a priori algorithm 2
A-Priori Algorithm --- (2)
  • Pass 1: Read baskets and count in main memory the occurrences of each item.
    • Requires only memory proportional to #items.
  • Pass 2: Read baskets again and count in main memory only those pairs both of which were found in Pass 1 to be frequent.
    • Requires memory proportional to square of frequent items only.
picture of a priori
Picture of A-Priori

Item counts

Frequent items

Counts of

candidate

pairs

Pass 1

Pass 2

detail for a priori
Detail for A-Priori
  • You can use the triangular matrix method with n = number of frequent items.
    • Saves space compared with storing triples.
  • Trick: number frequent items 1,2,… and keep a table relating new numbers to original item numbers.
frequent triples etc
Frequent Triples, Etc.
  • For each k, we construct two sets of k –tuples:
    • Ck= candidate k – tuples = those that might be frequent sets (support >s ) based on information from the pass for k –1.
    • Lk = the set of truly frequent k –tuples.
slide40

Filter

Filter

Construct

Construct

C1

L1

C2

L2

C3

First

pass

Second

pass

a priori for all frequent itemsets
A-Priori for All Frequent Itemsets
  • One pass for each k.
  • Needs room in main memory to count each candidate k –tuple.
  • For typical market-basket data and reasonable support (e.g., 1%), k = 2 requires the most memory.
frequent itemsets 2
Frequent Itemsets --- (2)
  • C1 = all items
  • L1 = those counted on first pass to be frequent.
  • C2 = pairs, both chosen from L1.
  • In general, Ck = k –tuples each k –1 of which is in Lk-1.
  • Lk = those candidates with support ≥s.