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Published byTheodora Douglas Modified over 9 years ago
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Apriori Algorithms Feapres Project
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Outline 1.Association Rules Overview 2.Apriori Overview – Apriori Advantage and Disadvantage 3.Apriori Algorithms – Step1 – Generate Frequent Items Set – Step 2 – Generate Rules 4.Improvement – 4.1. Segmental Values (mờ hóa dữ liệu) – 4.2. Get Support (Speed up algorithms) – 4.3. Weight Rules (Find important rules)
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1. Association Rules Overview Association Rule : relations between variables in large databases. Eg (Bread, Butter) => (Milk) Algorithms for finding association rules – Apriori algorithm : – Eclat algorithm – FP-growth algorithm – One-attribute-rule – Zero-attribute-rule
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2. Apriori Overview Best-known algorithm to mine association rules Advantages – Find all rules – Simple Disadvantages – Suffers from a number of inefficiencies or trade- offs – Operate in binary data only
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3. Apriori Algorithms Find all frequent itemsets: – Get frequent items: Items whose occurrence in database is greater than or equal to the min support. – Get frequent itemsets: Generate candidates from frequent items. Use the candidate to find the frequent itemsets. Repeat until there are no new candidates. Generate strong association rules from frequent itemsets – Rules which satisfy the min support and min confidence.
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3. Apriori Algorithms
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3.1 Apriori Algorithms : Step1 Transaction ACD BCE ABCE BE L1-ItemsetSupport {A}2 {B}3 {C}3 {E}3 Min Support = 50 % Min Confidence = 80% L2-ItemsetSupport {AC}2 {BC}2 {BE}3 {CE}2 ItemSupport {AB}1 {AC}2 {AE}1 {BC}2 {BE}3 {CE}2 Joint Check Support ItemSupport {A}2 {B}3 {C}3 {D}1 {E}3 Check Support
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3.1 Apriori Algorithms : Step1 L2-ItemsetSupport {AC}2 {BC}2 {BE}3 {CE}2 ItemSupport {BCE}2 Joint Check Support L3-ItemsetSupport {BCE}2 All subset of frequent Items must be frequent {ABCDEF} must combine with itemsets like {ABCDEG}
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3.1 Apriori Algorithms : Step1 Frequent ItemsSupport {A}2 {B}3 {C}3 {E}3 {AC}2 {BC}2 {BE}3 {CE}2 {BCE}2
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3.2 Apriori Algorithms : Step2
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4. IMPROVEMENT 4.1. Segmental Values (mờ hóa dữ liệu) 4.2. Get Support (Speed up algorithms) 4.3. Weight Rules (Find important rules)
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4.1. Segmental Values Major disadvantage of Apriori Algorithms is that it must work on binary database. -> Must convert conventional database to binary database Value Types – Category values – Continuous values (eg. Age, money, ….)
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4.1. Segmental Values Fuzzy Set – Triangle Function 0 1 a b c
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4.1. Segmental Values Fuzzy Set ―Trapezoid Function 0 1 ab cd
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4.1. Segmental Values Age values (0->100) – Young = F1(x,0,0,20,25) (red line) – Middle = F2(x,20,30,40,45) (blue line) – Old = F3(x,40,45,100,100) (yellow line) – MinWT = 0.4 0 1 2025304045100 Example : if F1(43) = 0; F2(43) = 0.5; F3(43) = 0.6) => 43 year old person is consider as both Middle and Old
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4.2. Get Support This procedure is the most time consuming part in the algorithms. L1-ItemsetSupport {A}2 {B}3 {C}3 {E}3 L2-ItemsetSupport {AC}2 {BC}2 {BE}3 {CE}2 ItemSupport {AB}1 {AC}2 {AE}1 {BC}2 {BE}3 {CE}2 Joint Check Support ItemSupport {A}2 {B}3 {C}3 {D}1 {E}3 Check Support
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4.2. Get Support Transaction ACDE BCE ABCE BCE AB SETElements A{1,3,5} B{2,3,4,5} C{1,2,3,4} D{1} E{1,2,3,4} => Need algorithms to calculate intersection of two set (HASH SET)
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4.3. Weight Rules Rules are in form: A => B Eg: (Buying time = Morning & Buying Method = Online => Bill Amount = High) Some component are more interested than others (such as Bill Amount) => Each component is weighted Importance of rule A=>B is
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THANKS FOR YOUR ATTENTION
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