Association Rule Mining Using WEKA

Association Rule Mining Using WEKA

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  ASSOCIATION RULE MINING USINGWEKA PREPARED BY TAZEEN TASNEEM TABEEN TASNEEM
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  A-PRIORI ALGORITHM • Aclassical algorithm. • Used for mining frequent item sets and relevant association rules. • Uses a “bottom up" approach. • Devised to operate on a database containing a lot of transactions. • Produces association rules. Implementing A-priori algorithm using weka 12/10/2018 2
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  ATTRIBUTE TYPES INA-PRIORI For running a-priori algorithm all attribute type must be one of these –  Nominal  Binary  Unary Implementing A-priori algorithm using weka 12/10/2018 3
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  ASSOCIATION RULE • Aprominent and well-explored method for determining relations among variables in large databases. • Helps to uncover relationships between seemingly unrelated data in a relational database. • It has two parts –  Antecedent (if)  Consequent (then) • Example – Let us consider an association rule be {Onion, Potato} => {Burger} which means that if onion and potato are bought, customers also buy a burger. • Created by analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships. Implementing A-priori algorithm using weka 12/10/2018 4
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  SUPPORT • The supportof an itemset X, supp(X) is the proportion of transaction in the database in which the item X appears. It signifies the popularity of an itemset. • 𝑠𝑢𝑝𝑝 𝑋 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑖𝑛 𝑤ℎ𝑖𝑐ℎ 𝑋 𝑎𝑝𝑝𝑒𝑎𝑟𝑠 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛𝑠 Implementing A-priori algorithm using weka 12/10/2018 5
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  CONFIDENCE • Signifies thelikelihood of item Y being purchased when item X is purchased. • 𝑐𝑜𝑛𝑓 𝑋 → 𝑌 = 𝑠𝑢𝑝𝑝(𝑋 ∪ 𝑌) 𝑠𝑢𝑝𝑝(𝑋) Implementing A-priori algorithm using weka 12/10/2018 6
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  AVAILABLE TOOLS More populartools used for data mining are – • Weka • Keel In this presentation, we will use WEKA data mining tool. Implementing A-priori algorithm using weka 12/10/2018 7
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  WHAT IS WEKA •Waikato Environment for Knowledge Analysis (Weka). • A collection of machine learning algorithms for data mining tasks. • Contains tools for data –  pre-processing  classification  Regression  Clustering  association rules  visualization. • An open source software issued under the GNU General Public License. Implementing A-priori algorithm using weka 12/10/2018 8
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  DATASET IN WEKA •Data set can be -  CREATED  DOWNLOAED • For this presentation, we have created our own dataset using Microsoft Excel Implementing A-priori algorithm using weka 12/10/2018 9
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  CREATING DATASET INMICROSOFT EXCEL Implementing A-priori algorithm using weka 12/10/2018 10
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  CREATING .CSV FILE ImplementingA-priori algorithm using weka 12/10/2018 11
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  CREATING .ARFF FILE ImplementingA-priori algorithm using weka 12/10/2018 12
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  WEKA INTERFACE Implementing A-priorialgorithm using weka 12/10/2018 13
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  LOADING (.ARFF) FILEIN WEKA Implementing A-priori algorithm using weka 12/10/2018 14
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  APPLYING ASSOCIATION RULE InWEKA, a-priori algorithm is default association rule. Before running a-priori algorithm we have checked if all attributes are nominal or binary or unary. Implementing A-priori algorithm using weka 12/10/2018 15
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  RUNNING A-PRIORI ALGORITHM ImplementingA-priori algorithm using weka 12/10/2018 16
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  RUNNING A-PRIORI ALGORITHM ImplementingA-priori algorithm using weka 12/10/2018 17
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  ASSOCIATOR OUTPUT Implementing A-priorialgorithm using weka 12/10/2018 18
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  INTERPRETATION OF RULES Letus interpret our first rule where the rule is – age = aged 5 ==> purchase = willBuy 5 Defines, if age = aged, then there are 5 incidents where purchase = willBuy. Implementing A-priori algorithm using weka 12/10/2018 19
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  GRAPHICAL REPRESENTATION 12/10/2018Implementing A-priorialgorithm using weka 20
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  12/10/2018Implementing A-priori algorithmusing weka 21
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  JITTER 12/10/2018Implementing A-priori algorithmusing weka 24 • Jitter function –  adds artificial random noise to the coordinates of the plotted points  spread the data out a bit.