Oil up observe stock movements every day clustering

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Oil-UP Observe Stock Movements every day.  Clustering points: Stock-{UP/DOWN} Similarity Measure: Two points are more similar if the events  described by them frequently happen together on the same day.  We used association rules to quantify a similarity measure.
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 29 Association Rule Discovery: Definition Given a set of records each of which contain some number of items from a given collection; Produce dependency rules which will predict occurrence of an item based on occurrences of other items. TID Items 1 Bread, Coke, Milk 2 Beer, Bread 3 Beer, Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk Rules Discovered: {Milk} --> {Coke}     {Diaper, Milk} --> {Beer} Rules Discovered: {Milk} --> {Coke}     {Diaper, Milk} --> {Beer}
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 30 Association Rule Discovery: Application 1 Marketing and Sales Promotion: Let the rule discovered be {Bagels, … } --> {Potato Chips} Potato Chips as consequent => Can be used to determine what should be done to boost its sales. Bagels in the antecedent => C an be used to see which products would be affected if the store discontinues selling bagels. Bagels in antecedent and Potato chips in consequent => Can be used to see what products should be sold with Bagels to promote sale of Potato chips!
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 31 Association Rule Discovery: Application 2 Supermarket shelf management. Goal: To identify items that are bought together by sufficiently many customers. Approach: Process the point-of-sale data collected with barcode scanners to find dependencies among items. A classic rule -- If a customer buys diaper and milk, then he is very likely to buy beer. So, don’t be surprised if you find six-packs stacked next to diapers!
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 32 Association Rule Discovery: Application 3 Inventory Management: Goal: A consumer appliance repair company wants to anticipate the nature of repairs on its consumer products and keep the service vehicles equipped with right parts to reduce on number of visits to consumer households. Approach: Process the data on tools and parts required in previous repairs at different consumer locations and discover the co-occurrence patterns.
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 33 Sequential Pattern Discovery: Definition Given is a set of objects , with each object associated with its own timeline of events , find rules that predict strong sequential dependencies among different events. Rules are formed by first disovering patterns. Event occurrences in the patterns are governed by timing constraints.
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