SLIDE 1
BY: CANDACE MCQUEEN
ASSOCIA IATIO ION means they have:
Ø a common purpose and having a formal structure Ø A connection or a combination Ø Friendship Ø A Correlation
ASSOCIATION BY: CANDACE MCQUEEN ASSOCIA IATIO ION means they have - - PowerPoint PPT Presentation
ASSOCIATION BY: CANDACE MCQUEEN ASSOCIA IATIO ION means they have - - PowerPoint PPT Presentation
ASSOCIATION BY: CANDACE MCQUEEN ASSOCIA IATIO ION means they have : a common purpose and having a formal structure A connection or a combination Friendship A Correlation Their close association did not last long.
SLIDE 2
SLIDE 3
Their close association did not last long.
SLIDE 4
………Some examples of Associations……
SLIDE 5
A FIST IN THE FACE>>>>>>>>>>BRUISE
SLIDE 6
SLIDE 7
An association mining problem can be decomposed using APRIO IORI…..
Wha hat A APRIO IORI d I does… Association R n Rule les
Calculate rules that express
the probable co-occurrence
- f items within frequent item
sets.
Apriori calculates the
probability of an item being present in a frequent item set, given that another item or items is present
The Apriori algorithm
calculates rules that express probabilistic relationships between items in frequent item sets. For example, a rule derived from frequent item sets containing A, B, and C might state that if A and B are included in a transaction, then C is likely to also be included.
SLIDE 8
>An Association Rule states that an item
- r group of items implies the presence of
another item with some probability. >Unlike decision tree rules, which predict a target, association rules simply express correlation.
SLIDE 9
Antecedent and Consequent IF IF……. . THEN……. .
The IF component of an
association rule is known as the antecedent.
The THEN component
is known as the consequent. ü The antecedent and the consequent are disjoint; they have no items in common.
SLIDE 10
SLIDE 11
Items on the Sonic menu……
SLIDE 12
INFORMATION ABOUT MY DATA……
DATA TAKEN ON NOV
. 30 2011
21 ATTRIBUTES
Breakfast 1, Breakfast 2, burger 1, burger 2,
burger 3 , WP 1, WP 2, CHK 1, CHK 2, SWAMP 1, SWAMP 2, SWAMP 3, SWAMP 4, SIDE 1 SIDE2, SIDE 3, FOUNTAIN 1, FOUNTAIN 2, FOUNTIAN 3, FOUNTAIN 4, FROZEN 1
99 TICKETS/RECIEPTS
SLIDE 13
……….PLEASE LOOK AT THE RECIEPT THAT I HAVE GIVEN YOU………
SLIDE 14
SLIDE 15
SLIDE 16
Breakfast 1…..
SLIDE 17
Burger 1……
SLIDE 18
SLIDE 19
Rules…..
SLIDE 20
PRE P PROCESSIN ING D G DATA … ….. ..
PREPROCESSING steps should be applied to
make the data more suitable for results
Increases/higher Support Taking out inferences that will not affect the
data that is be sought for
HAPPY HOUR .99 LG. BEVERAGES Strip minority combinations out Chocolate milk (2)
SLIDE 21
CONTINUATION OF PRE PROCESSING DATA…… Issues:
Small set/One day Short coming- ability to handle large data
sets.
Errors occurring Much manual labor First time using this program
SLIDE 22
REFERENCES
"Association - Google Search." Google. Web. 13 Dec. 2011.
<http://www.google.com/search?q=association>.
Chen, Victoria C. P
. Data Mining. Dordrecht, Netherlands: Springer, 2010. Print.
"A Priori and a Posteriori." Wikipedia, the Free Encyclopedia.
. 13 Dec. 2011. <http://en.wikipedia.org/ wiki/A_priori_and_a_post eriori>.
Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar. I
ntroduction to Data Mining. Boston: Pearson Addison Wesley, 2005. Print.