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Data Mining

Data Mining. Jim. Which cow should I buy??. Jim ’ s cows. Cows on sale. Which cow should I buy??. Which cow should I buy??. And suppose I know their: Behavior Preferred mating months Milk production Nutritional habits Immune system data … Now suppose I have 10,000 cows ….

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Data Mining

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  1. Data Mining

  2. Jim Which cow should I buy??

  3. Jim’s cows Cows on sale Which cow should I buy??

  4. Which cow should I buy?? • And suppose I know their: • Behavior • Preferred mating months • Milk production • Nutritional habits • Immune system data • … • Now suppose I have 10,000 cows…

  5. “understanding” data • Looking for patterns is not new: • Hunters seek patterns in animal migration • Politicians seek patterns in voting habits • … • Available data is increasing very fast (exponentially?) • Greater opportunities to extract valuable information • But “understanding” the data becomes more difficult

  6. Data Mining • Data Mining: The process of discovering patterns in data, usually stored in a Database. The patterns lead to advantages (economic or other). • Very fast growing area of research • Because: • Huge databases (Walmart-20 mil transactions/day) • Automatic data capture of transactions (Bar code, satellites, scanners, cameras, etc.) • Large financial advantage • Evolving analytical methods

  7. Data Mining techniques in some huji courses

  8. Data Mining • Two extremes for the expression of the patterns: • “Black Box”:“Buy cow Zehava, Petra and Paulina” • “Transparent Box” (Structural Patterns):“Buy cows with age<4 and weight >300 or cows with calm behavior and >90 liters of milk production per month”

  9. The weather example Today is Overcast, mild temperature, high humidity, and windy. Will we play?

  10. Questions one can ask • A set of rules learned from this data could be presented in a Decision List: • If outlook=sunny and humidity=high then play=no • ElseIf outlook=rainy and windy=true then play=no • ElseIf outlook=overcast then play=yes • ElseIf humidity=normal then play=yes • Else play=yes • This is an example of Classification Rules • We could also look for Association Rules: • If temperature=cool then humidity=normal • If windy=false and play=no then outlook=sunny and humidity=high

  11. Example Cont. • The previous example is very simplified. Real Databases will probably: • Contain Numerical values as well. • Contain “Noise” and errors (stochastic). • Be a lot larger. • And the analysis we are asked to perform might not be of Association Rules, but rather Decision Trees, Neural Networks, etc.

  12. Caution • David Rhine was a parapsychologist in the 1930-1950’s • He hypothesized that some people have Extra-Sensory Perception (ESP) • He asked people to say if 10 hidden cards are red or blue. • He discovered that almost 1 in every 1000 people has ESP ! • He told these people that they have ESP and called them in for another test • He discovered almost all of them had lost their ESP ! • He concluded that… • You shouldn’t tell people they have ESP, it makes them loose it. [Source: J. Ullman]

  13. Another Example • Classic example: Database of purchases in a supermarket • Such huge DB’s which are saved for long periods of time are called Data Warehouses • It is extremely valuable for the manager of the store to extract Association Rules from the Data Warehouse • It is even more valuable if this information can be associated with the person buying, hence the Club Memberships… • Each Shopping Basket is a list of items that were bought in a single purchase by some customer

  14. Supermarket Example • Beer and Diapers were found to often be bought together by men, so they were placed in the same aisle [Datamining urban legend]

  15. The Purchases Relation Itemset: A set of items Support of an itemset: the fraction of transactions that contain all items in the itemset. • What is the Support of: • {pen}? • {pen, ink}? • {pen, juice}?

  16. Frequent Itemsets • We would like to find items that are purchased together at high frequency- Frequent Itemsets. • We look for itemsets which have a support > minSupport. • If minSupport is set to 0.7, then the frequent itemsets in our example would be: {pen}, {ink}, {milk}, {pen, ink}, {pen, milk} • The A-Priori property of frequent itemsets: Every subset of a frequent itemset is also a frequent itemset.

  17. Algorithm for finding Frequent itemsets • Suppose we have n items. • The naïve approach: for every subset of items, check if it is frequent. • Very expensive • Improvement (based on the A-priori property): first identify frequent itemsets of size 1, then try to expand them. • Greatly reduces the number of candidate frequent itemsets. • A single scan of the table is enough to determine which candidate itemsets, are frequent. • The algorithm terminates when no new frequent itemsets are found in an iteration.

  18. Algorithm for finding Frequent itemsets foreach item, check if it is a frequent itemset; (appears in >minSupport of the transactions) k=1; repeat foreach new frequent itemset Ik with k items: Generate all itemsets Ik+1 with k+1 items, such that Ik is contained inIk+1. scan all transactions once and add itemsets that have support > minSupport. k++ until no new frequent itemsets are found

  19. Finding Frequent itemsets, on table “Purchases”, with minSupport=0.7 • In the first run, the following single itemsets are found to be frequent: {pen}, {ink}, {milk}. • Now we generate the candidates for k=2: {pen, ink}, {pen, milk}, {pen, juice}, {ink, milk}, {ink, juice} and {milk, juice}. • By scanning the relation, we determine that the following are frequent: {pen, ink}, {pen, milk}. • Now we generate the candidates for k=3: {pen, ink, milk}, {pen, milk, juice}, {pen, ink, juice}. • By scanning the relation, we determine that none of these are frequent, and the algorithm ends with: { {pen}, {ink}, {milk}, {pen, ink}, {pen, milk} }

  20. Algorithm refinement: • One important refinement: after the candidate-generation phase, and before the scan of the relation (A-priori), eliminate candidate itemsets in which there is a subset which is not frequent. This is due to the A-Priori property. • In the second iteration, this means we would eliminate {pen, juice}, {ink, juice} and {milk, juice} as candidates since {juice} is not frequent. So we only check {pen, ink}, {pen, milk} and {ink, milk}. • So only {pen, ink,milk} is generated as a candidate, but it is eliminated before the scan because {ink, milk} is not frequent. • So we don’t perform the 3rd iteration of the relation. • More complex algorithms use the same tools: iterative generation and testing of candidate itemsets.

  21. Association Rules • Up until now we discussed identification of frequent item sets. We now wish to go one step further. • An association rule is of the structure {pen}=> {ink} • Meaning: “if a pen is purchased in a transaction, it is likely that ink will also be purchased in that transaction”. • It describes the data in the DB (past). Extrapolation to future transactions should be done with caution. • More formally, an Association Rule is LHS=>RHS, where both LHS and RHS are sets of items, and implies that if every item in LHS was purchased in a transaction, it is likely that the items in RHS are purchased as well.

  22. Measures for Association Rules • Support of “LHS=>RHS”is the support of the itemset (LHS U RHS). In other words: the fraction of transactions that contain all items in (LHS U RHS) . • Confidence of “LHS=>RHS”: Consider all transactions which contain all items in LHS. The fraction of these transactions that also contain all items in RHS, is the confidence of RHS. =S(LHS U RHS)/S(LHS) • The confidence of a rule is an indication of the strength of the rule.

  23. What is the support of {pen}=>{ink}? And the confidence? What is the support of {ink}=>{pen}? And the confidence?

  24. Finding Association rules • A user can ask for rules with minimum support minSup and minimum confidence minConf. • Firstly, all frequent itemsets with support>minSup are computed with the previous Algorithm. • Secondly, rules are generated using the frequent itemsets, and checked for minConf.

  25. Finding Association rules • Find all frequent itemsets using the previous alg. • For each frequent itemset X with support S(X): For each division of X into 2 itemsets, LHS and RHS: Calculate the Confidence of LHS=>RHS : S(X)/S(LHS). • We computed S(LHS) in the previous algorithm (because LHS is frequent since X is frequent).

  26. Generalized association rules We would like to know if the rule {pen}=>{juice} is different on the first day of the month compared to other days. How? What are its support and confidence generally? And on the first days of the month?

  27. Generalized association rules • By specifying different attributes to group by (date in the last example), we can come up with interesting rules which we would otherwise miss. • Another example would be to group by location and check if the same rules apply for customers from Jerusalem compared to Tel Aviv. • By comparing the support and confidence of the rules we can observe differences in the data on different conditions.

  28. Caution in prediction • When we find a pattern in the data, we wish to use it for prediction (that is in many case the point). • However, we have to be cautious about this. • For example: suppose {pen}=>{ink} has a high support and confidence. We might give a discount on pens in order to increase sales of pens and therefore also in sales of ink. • However, this assumes a causal link between {pen} and {ink}.

  29. Caution in prediction • Suppose pens and pencils are sold together a lot • We would then also get the rule {pencil}=>{ink} with high support and confidence • However, it is clear there is no causal link between buying pencils and buying ink. • If we promoted pencils it would not cause an increase in sales of ink, despite high support and confidence. • The chance to infer “wrong” rules (rules which are not causal links) decreases as the DB size increases, but we should keep in mind that such rules do come up. • Therefore, the generated rules are a only good starting point for identifying causal links.

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