Data Quality Class 9. Agenda Project – 4 weeks left Exam – Rule Discovery Data Linkage.

Data Quality

Class 9

Agenda

• Project – 4 weeks left

• Exam –

• Rule Discovery

• Data Linkage

Project

• Please email me a schema by Friday

• I will review the schema

• By next Friday:– Application to parse a file and generate table

entries

Exam

• Answers to questions

Rule Discovery

• Decision and ClassificationTrees

• Association Rules

Decision and Classification Trees

• Each node in tree represents a question• Decision as to which path to take from that

node is dependent on the answer to the question

• At each step along the path from the root of the tree to the leaves, the set of records that conform to the answers along the way continues to grow smaller

Decision and Classification – 2

• At each node in the tree, we have a representative set of records that conform to the answers to the questions along that path

• Each node in the tree represents a segmenting question, which subdivides the current representative set into two smaller segments

• Every path is unique• Each node in the tree also represents the

expression of a rule

mammal?

yes no

domesticated?

lives in a cage? carnivore?

rodent? barks?

long snout amphibian?

yes

yes yesno no yes yesno noyes yesno no yes yesno no yes yesno no

no yes no yes no yes no

yes noyes no

rat

rabb

it

lion

dog

cat

feline? long neck?

hyen

a

gira

ffe

hipp

o

reptile?

Everglades? Hawaii? bumpy skin? flies?

Allig

ator

Cro

codi

le

toad

Gec

ko

Kom

odo

Dra

gon

sala

man

der

peng

uin

eagl

e

Example

Decision and Classification – 3

CART

• Classification and Regression Tree• Grab a training set• Subselect some records that we know already

share some attribute properties in common • All other data attributes become independent

variables• The results of the decision tree process are to be

applied to the entire data set at a later date

CART 2

• Decide which of the independent variables is the best for splitting the records

• The choice for the next split is based on choosing the criteria that divide the records into sets where, in each set, a single characteristic predominates

• Evaluate the possible ways to split based on each independent variable, measuring how good that split will be

Selection Heuristics

• Gini: maximize the set differentiated by a split, with the goal of isolating records with that class from other records

• Twoing: tries to evenly distribute the records at each split opportunity

• There are other heuristics

CART 3

• The complete tree is built by recursively splitting the data at each decision point in the tree

• At each step, if we find that for a certain attribute all values are the same, we eliminate that attribute from future consideration

• When we reach a point where no appropriate split can be found, we determine that node to be a leaf node

• When the tree is complete, the splitting properties at each internal node can be evaluated and assigned some meaning

Example 2

Monthly bill > $100

Buys < 2 Pay-per-Views

Buys 3 or 4 Pay-per-Views

Buys > 4 Pay-per-Views

yes no

Rules

• If (monthly_bill > 100) AND (PayPerViews < 2)• If (monthly_bill > 100) AND (PerPerViews > 2)

AND (PayPerViews < 5)• If (monthly_bill > 100) AND (PayPerViews >= 5)

Association Rules

• Rules of the form X Y, where X is a set of (attribute, value) pairs and Y is a set of (attribute, value) pairs

• An example is “94% of the customers that purchase tortilla chips and cheese also purchase salsa”

• This can be used for many application domains, such as market basket analysis

• Can also be used to discover data quality rules

Association Rules 2

• Formally:– Let D be a database of records

– Each record R in D contains a set of (attribute, value) pairs (also called an item)

– An itemset X is a subset of (attribute, value) pairs of a record R (i.e., X R)

– An association rule is an implication of the form X Y, where X and Y are both itemsets, and share no attributes.

– The rule holds with confidence c if c% of the records that contain X also contain Y

– The rule has support s% if s% of the records in D contain X or Y

Association Rules 3

• Confidence is the percentage of time that the rule holds when X is in the record

• Support is the percentage of time that the rule could hold

• Association rules describe a relation imposed on individual values that appear in the data

• Association rules with high confidence are likely to imply generalities about the data

• We can infer data quality rules from the discovery of association rules

Association Rules 4

• Example:– (CustomerType==Business) AND (total > $1000)

(managerApproval == “required”) with confidence 85% and support 25%

– This means that 25% of the time, the record had one of those attributes set with the indicated values

– Of the records with (CustomerType==Business) AND (total > $1000), 85% of the time he attribute managerApproval had the value “required”

– We might infer this as a more general rule, that business orders greater than $1000 require manager approval

– This calls into question the 15% of the time it didn’t hold true – data quality problem, or is it not a general rule?

Association Rules 5

• We can set some minimum support and minimum confidence levels

• Definitions:– Lk is the set of large sets having k items

– Ck is the set of candidate sets having k items

Association Rules Algorithm

• L1 = sets with 1 item

• for (k = 2; Lk-1 not empty; k++) do

– Ck = generate_new_candidates(Lk-1)

– forall records R in D do• CR = subset(Ck, R)

• forall candidates c in CR do

– c.count++;

– end

• Lk = {c in CR | c.count > minimum support}

Candidate Generation and Subset

• Takes the set of all large itemsets of size (k – 1)

• First, it joins Lk-1 with Lk-1, if they share (k – 2) items, to get a superset of the set of candidates

• The candidates are pruned if a subset of the items in each candidate does not have minimum support (i.e., the subset of size (k – 1) is not in Lk-1

• Subset operation takes a record, and finds all candidate rules of iteration k within that record

More on Association Rules

• We can adjust our goals for finding rules by quantizing the values in each attribute– In other words, we can assign values of attributes that belong to

large ranges into quantized components, making the rule process less cumbersome

• We can also use clustering to ehnace the association rule algorithm– If we don’t know how to quantize to begin with, use clustering for

values

• Association rules can uncover interesting data quality and business rules

Record Linkage

• Critical component of data quality applications• Linkage involves finding a link between a pair of

records, either through an exact match or through an approximate match

• Linkage is useful for – data cleansing– data correction– enhancement– householding

Record Linkage 2

• Two records are linked when they match with enough weighted similarity

• Matching can be a combination of exact matching on particular fields, to approximat ematching with some degree of similarity

• For example: two customer records can be linked via an account number, if account numbers are uniquely assigned to customers

Record Linkage 3

• Example:– David Loshin– 633 Evergreen Terrace– Montclair, NJ– 201-765-8293

vs.– H. David Loshin– 633 Evergreen– Montclair, NJ– 201-765-8293

• In this case, we can establish a link based solely on telephone number

Record Linkage 4

• Frequently, pivot attributes exists and can be used for exact matching (such as social security number, account number, telephone number, student ID)

• Often, there is no pivot attribute, and therefore approximate matching techniques must be used

• Approximate matching is a process of looking for similarity

Similarity

• As with clustering, we use measures of similarity to establish measures and thresholds for linkage

• Most interesting areas for similarity are in string matching

String Similarity

• How do we characterize similarity between strings?

• We can see it with our eyes, or hear it inside our heads:– example: Smith, Smythe

– example: John and Mary Jones, John Jones

• How do we transfer this to automation?

Edit Distance

• Edit distance operations– Insertion, where an extra character is inserted into the

string

– Deletion, where a character has been removed from the string

– Transposition, in which two characters are reversed in their sequence

– Substitution, which is an insertion followed by a deletion

Edit Distance 2

• Strings with a small edit distance are likely to be similar

• Edit distance is measured as a count of edit distance operations from one string to another:– internatianl

» transposition

– internatinal» insertion

– international

• internatianl to international has an edit distance of 2

Computing Edit Distance

• Use dynamic programming

• Given two strings, x = x1x2 .. xn, and y = y1y2 ..ym

• the edit distance f(i, j) is computed as the best match of two substrings x1x2 .. xi and y1y2 ..yj where

– f(0,0) = 0

– f(i, j) = min[f(i-1, j) + 1), f(i, j-1) +1, f(i-1, j-1) + d(xi,yj)]

Phonetic Similarity

• Words that sound the same may be misspelled• Phonetic similarity reduces the complexity of the

strings• Effectively, it compresses the strings, with some

loss, then performs a similarity test

Soundex

• The first character of the name string is retained, and then numbers are assigned to following characters

• The numbers are assigned using this breakdown:– 1 = B P F V

– 2 = C S K G J Q X Z

– 3 = D T

– 4 = L

– 5 = M N

– 6 = R

• Vowels are ignored

Soundex – 2

• Examples:– Fitzgerald = F322

– Smith = S530

– Smythe = S530

– Loshin = L250

Soundex – 3

• Regular soundex is flawed– geared towards English names– can’t account for incorrect first letter– longer names are truncated

• Options– encode entire string, not just the first 4 consonant

sounds– reverse the words, then encode both forward and

backward– Use different phonetic encodings

Other Phonetic Encoding

• NYSIIS– Similar to soundex, but

• does not use numeric encoding, instead uses mapping to smaller set of consonants

• replaces all vowels with “A”

• Metaphone– Tries to be more exact with multiple-letter sounds (sh,

tio, th, etc.)

N-gramming

• Another means of representing a “compressed” form of a string

• An n-gram is a chunk of text of length n• We slide a window of size n across a string to

generate the set of n-grams for that string

N-gram Example

• INTERNATIONAL is comprised of these 2-grams:– IN, NT, TE, ER, RN, NA, AT, TI, IO,ON, NA, AL

• Compare this with INTERNATIANL– IN, NT, TE, ER, RN, NA, AT, TI, IA, AN, NL

• These two strings share 8 2-grams

Data Quality Class 9. Agenda Project – 4 weeks left Exam – Rule Discovery Data Linkage.

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