DTSA 5505 - Data Mining Methods – study cards.pdf DTSA 5505 - Data Mining Methods – study cards.pdf DTSA 5505 - Data Mining Methods – study cards.pdf
DTSA 5505 - Data Mining
Methods – study cards
DTSA 5505Guidehttps://www.stuvia.com/dashboard!@_)#*)(@$)($@*($@)($@*_
- Data Mining Methods – study cards.pdf DTSA 5505 - Data Mining Methods – study cards.pdf DTSA 5505 - Data Mining Methods – study cards.pdf
,DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf
Terms in this set (103)
market basket analysis an unsupervised data mining technique for determining sales patterns
Frequent Pattern Mining closed pattern X = no super-pattern Y given X with the same support; max-
pattern X = no super-pattern Y given X
Apriori Algorithm A fast method of finding frequent itemsets, which also involves pruning non-
frequent items and self-joining of k-itemsets only if their first (k-1) items are
the same.
What challenges are there with apriori algorithm? multiple scans of whole dataset, huge number of candidates, support
counting of all candidates
Improvements for apriori algorithm? Partitioning, Sampling, Transaction reduction
Vertical data format Mining frequent itemsets using the ________________________ is a method that
transposes the rows of a given data set into columns.
DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf
, DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf
FP-Growth Algorithm If 'd' is frequent in DB | abc, then abcd is frequent (avoid candidate
generation)
Association Rules Association rules specify a relation between attributes that appears more
frequently than expected if the attributes were independent.
Correlation rules Measure of dependent/correlated events: lift(A,B) = P(A U B) / P(A)P(B)
Rules of lift (correlation) lift = 1 (independent), lift > 1 (positive), lift < 1 (negative)
Metarule-Guided Mining P1 and P2 and ..... and Px => Q1 and Q2 and .... and Qy
Supervised Learning Predefined classes, training data with groundtruth label
Unsupervised Learning No predefined classes; aims to identify potential clusters/patterns
Classification categorical class labels (e.g. fraud detection)
Prediction Continuous numerical values (e.g. stock prices)
DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf
DTSA 5505 - Data Mining
Methods – study cards
DTSA 5505Guidehttps://www.stuvia.com/dashboard!@_)#*)(@$)($@*($@)($@*_
- Data Mining Methods – study cards.pdf DTSA 5505 - Data Mining Methods – study cards.pdf DTSA 5505 - Data Mining Methods – study cards.pdf
,DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf
Terms in this set (103)
market basket analysis an unsupervised data mining technique for determining sales patterns
Frequent Pattern Mining closed pattern X = no super-pattern Y given X with the same support; max-
pattern X = no super-pattern Y given X
Apriori Algorithm A fast method of finding frequent itemsets, which also involves pruning non-
frequent items and self-joining of k-itemsets only if their first (k-1) items are
the same.
What challenges are there with apriori algorithm? multiple scans of whole dataset, huge number of candidates, support
counting of all candidates
Improvements for apriori algorithm? Partitioning, Sampling, Transaction reduction
Vertical data format Mining frequent itemsets using the ________________________ is a method that
transposes the rows of a given data set into columns.
DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf
, DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf
FP-Growth Algorithm If 'd' is frequent in DB | abc, then abcd is frequent (avoid candidate
generation)
Association Rules Association rules specify a relation between attributes that appears more
frequently than expected if the attributes were independent.
Correlation rules Measure of dependent/correlated events: lift(A,B) = P(A U B) / P(A)P(B)
Rules of lift (correlation) lift = 1 (independent), lift > 1 (positive), lift < 1 (negative)
Metarule-Guided Mining P1 and P2 and ..... and Px => Q1 and Q2 and .... and Qy
Supervised Learning Predefined classes, training data with groundtruth label
Unsupervised Learning No predefined classes; aims to identify potential clusters/patterns
Classification categorical class labels (e.g. fraud detection)
Prediction Continuous numerical values (e.g. stock prices)
DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf DTSA 5505 - Data Mining Methods (Study Cards).pdf
