Chapter 6 test deck

Question 1

frquent pattern

Answer 1

A set of items subsequences substructures that occure frequently in a data set
an intrinsic and important p[roprty if dataset

Question 2

frequent itemset

Answer 2

a set of items that appear frequently together in a transaction data set, e.g. milk and bread

Question 3

grquent sequential pattern

Answer 3

buying your first pc, then a digital camera and then a memory car

Question 4

substructures

Answer 4

refer to different strucural forms such as subgraphs, subtrees, or sublattice, which may be combined with itemsets or subsequences. If a substructure occurs frequently it is called a frquent structured pattern

Question 5

fequent pattern mining

Answer 5

searches for recurring relationships in a given data setand is the foundation fro many essential data mining tasks

Question 6

market basket analysis

Answer 6

the earliest form of frequent pattern mining for association rule

Question 7

association rule

Answer 7

each item has a boolean variable representing the presence or absence of that item
each baskit can then be represented by a boolean vector of values

Question 8

support

Answer 8

usefulness

Question 9

confidence

Answer 9

certainty of rules
the teliability of the inference made by a rule
the higher the confidence the more likely it is for b to be present in transactions that contain A

Question 10

itemset

Answer 10

a set of one or more items

Question 11

occurenace frequency

Answer 11

frquency of an itemset x, supportm suoport count, or count of the itemset

Question 12

relative support

Answer 12

the fraction of transaction that contain x

Question 13

Association rule does not necessarily imply causality

Question 14

mining association rules

Answer 14

1. find all frequent itemsets: each of these item sets will occur at least as frequently as a predetermined min support count
2. generate strong association rules from the frequent itemsets: these rules must satisfy minimum support and minimum confidence

Question 15

apriori property

Answer 15

all nonempty subsets of a frequent itemset must also be frequent

Question 16

antimonotonicity

Answer 16

if a set cannot pass a test, all of its supersets will fail the same test as well. The property is monotonic in the context of failing a test

Question 17

apriori

Answer 17

acandidate generation-and-test approch
uses horizontal data format
TID itemset format

Question 18

eclat

Answer 18

frquent pattern mining with vertical data format
equivalence class transformation algorithm
item TID _set format
the support count of an itemset is simply the length of the TID_set of the itemset

Question 19

Downward closue property of requent patterns

Answer 19

any subset of a frequent item set must be frequent
if beer, diaper, and nuts are frequent, beer and diapers must be too
every transaction having all three also contains just two

Question 20

scalable mining methods

Answer 20

1. apriori
2. frequent pattern growth not covered in class
3. vertical data format approach

Question 21

apriori pruning priciple

Answer 21

if ther is any itemset which is infrequent, its superset should not be generated or tested

Question 22

apriori method

Answer 22

initially scan DB once to get requent utemset
generate length cadidate itemsets form length k frequent itemsets
test the candidtis against DB
terminate when no frequent or candidate set can be generated

Question 23

Association rule generation

Answer 23

1. for each frequent item set, generate all non empty subsets of it
2. for every nonempty subsetS of I output rule s -> I-S if couport I / support S >= min

Question 24

patern evaluation method

Answer 24

strong association rules can be uniteresting and misleading

Question 25

correlation mesasure lift

Answer 25

the ocurrance of itemseta is independent of the occuence of item set b if p(aub) = p(a)P(b) otherwise items set a nad b are dependent and correlated as events

Question 26

negativly correlated

Answer 26

lift(a,b) is < 1 meaning the occuence of one likey lead to the absence of the other

Question 27

positivly correlated

Answer 27

lift a,b > 1 meaning that the occurance of one implies the occurrence of the other

Question 28

independent

Answer 28

lift a,b =1 there is no correlation between them