Data mining

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Data mining

  1. 1. Data Mining Rajendra AkerkarJuly 7, 2009 Data Mining: R. Akerkar 1
  2. 2. What Is Data Mining?• Data mining (knowledge discovery from data) – Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data• Is everything “data mining”? – (Deductive) query processing. processing – Expert systems or small ML/statistical programsJuly 7, 2009 Data Mining: R. Akerkar 2
  3. 3. Definition • Several Definitions – Non-trivial extraction of implicit, previously unknown and potentially useful information from data – Exploration & analysis, by automatic or semi automatic semi-automatic means, of large quantities of data in order to discover meaningful patternsJuly 7, 2009 Data Mining: R. Akerkar 3
  4. 4. From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996 [ yy , ] g y g,July 7, 2009 Data Mining: R. Akerkar 4
  5. 5. ClassificationJuly 7, 2009 Data Mining: R. Akerkar 5
  6. 6. Classification: D fi iti Definition• Given a collection of records (training set ) – Each record contains a set of attributes, one of the attributes is the class.• Find a model for class attribute as a function of the values of other attributes.• Goal: pre io sl unseen records should be assigned previously nseen sho ld a class as accurately as possible. – A test set is used to determine the accuracy of the model. y Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.July 7, 2009 Data Mining: R. Akerkar 6
  7. 7. Classification: Introduction• A classification scheme which generates a tree and a set of rules from given data set.• The attributes of the records are categorise into two types: – Attributes whose domain is numerical are called numerical attributes. – A ib Attributes whose domain is not numerical are h d i i i l called the categorical attributes. July 7, 2009 Data Mining: R. Akerkar 7
  8. 8. Decision Tree• A decision tree is a tree with the following properties: – An inner node represents an attribute. – A edge represents a t t on the attribute of the father An d t test th tt ib t f th f th node. – A leaf represents one of the classes.• Construction of a decision tree – Based on the training data – Top-Down strategyJuly 7, 2009 Data Mining: R. Akerkar 8
  9. 9. July 7, 2009 Data Mining: R. Akerkar 9
  10. 10. Decision Tree Example• The data set has five attributes.• There is a special attribute: the attribute class is the class label. label• The attributes, temp (temperature) and humidity are numerical attributes• Other attributes are categorical, that is, they cannot be categorical is ordered.• Based on the training data set, we want to find a set of set rules to know what values of outlook, temperature, humidity and wind, determine whether or not to play golf.July 7, 2009 Data Mining: R. Akerkar 10
  11. 11. Decision Tree Example• We have five leaf nodes.• In a decision tree, each leaf node represents a rule. , p• We have the following rules corresponding to the tree given in Figure.• RULE 1 If it is sunny and the humidity is not above 75%, then play.• RULE 2 If it is sunny and the humidity is above 75%, then do not play. f y y , p y• RULE 3 If it is overcast, then play.• RULE 4 If it is rainy and not windy, then play.• RULE 5 If it is rainy and windy, then dont play.July 7, 2009 Data Mining: R. Akerkar 11
  12. 12. July 7, 2009 Data Mining: R. Akerkar 12
  13. 13. Iterative Dichotomizer (ID3)• Quinlan (1986)• Each d E h node corresponds to a splitting attribute d t litti tt ib t• Each arc is a possible value of that attribute.• At each node the splitting attribute is selected to be the most informative among the attributes not yet considered in the path from the root.• Entropy is used to measure how informative is a node.• The algorithm uses the criterion of information gain to determine the goodness of a split. – The attribute with the greatest information gain is taken as g g the splitting attribute, and the data set is split for all distinct values of the attribute. July 7, 2009 Data Mining: R. Akerkar 13
  14. 14. Training Dataset This follows an example from Quinlan’s ID3 age income student credit_rating buys_computer <=30 high no fair no <=30 g high no excellent no 31…40 high no fair yes >40 medium no fair yes >40 low yes fair yes >40 low yes excellent no 31…40 low yes excellent yes <=30 medium no fair no <=30 low yes fair yes >40 medium yes fair yes <=30 medium yes excellent yes 31…40 medium no excellent yes 31…40 g high y yes fair y yes >40 medium no excellent noJuly 7, 2009 Data Mining: R. Akerkar 14
  15. 15. Extracting Classification Rules from Trees• Represent the k R h knowledge in the l d i h form of IF-THEN rules• One rule is created for each path from the root to a leaf• Each attribute-value pair along a path forms a conjunction• The leaf node holds the class prediction• Rules R l are easier for humans to i f h understand What are the rules?July 7, 2009 Data Mining: R. Akerkar 15
  16. 16. Attribute Selection Measure: InformationGain (ID3/C4.5) (ID3/C4 5) Select the attribute with the highest information gain S contains si tuples of class Ci for i = {1, …, m} information measures info required to classify any q y y arbitrary tuple m si si I( s1,s 2,...,s m )    log 2 i 1 s s ….information is encoded in bits. entropy of attribute A with values {a1,a2,…,av} f b h l { v s1 j  ... smj E(A)  I( s1 j ,...,smj ) j 1 s information gained by branching on attribute A Gain(A)  I(s 1, s 2 ,..., sm)  E(A)July 7, 2009 Data Mining: R. Akerkar 16
  17. 17. age pi ni I(pi, ni)  Class P: buys_computer = “yes”  Cl Class N: buys_computer = “no” N b t “ ”<=30 2 3 0.971  I(p, n) = I(9, 5) =0.94030…40 4 0 0  Compute the entropy for age:>40 3 2 0 971 0.971 age income student credit_rating buys_computer<=30 high g no fair no<=30 high no excellent no31…40 high no fair yes>40 medium no fair yes>40 low ow yes fair yes>40 low yes excellent no31…40 low yes excellent yes<=30 medium no fair no< 30<=30 low yes fair yes>40 medium yes fair yes<=30 medium yes excellent yes31…40 medium no excellent yes31…4031 40 high yes fair yes>40 medium no excellent no July 7, 2009 Data Mining: R. Akerkar 17
  18. 18. Attribute Selection by Information Gain Computation 5 4 E ( age )  g I ( 2 ,3)  I ( 4,0 ) 14 14 5  I (3, 2 )  0 .694 14 5 I ( 2 ,3 ) means “age <=30” has 5 out of 14 samples, with 2 yess14 and 3 no’s. Hence Gain ( age )  I ( p , n )  E ( age )  0.246 Similarly, Gain(income)  0.029 Gain( student )  0.151 Gain(credit _ rating )  0.048July 7, 2009 Data Mining: R. Akerkar 18
  19. 19. Exercise 1• The following table consists of training data from an employee database. database• Let status be the class attribute. Use the ID3 algorithm to construct a decision tree from the given data. July 7, 2009 Data Mining: R. Akerkar 19
  20. 20. ClusteringJuly 7, 2009 Data Mining: R. Akerkar 20
  21. 21. Clustering: Definition• Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that – Data points in one cluster are more similar to one another. – Data points in separate clusters are less similar to one another. h• Similarity Measures: – E lid Euclidean Distance if attributes are continuous. Di t tt ib t ti – Other Problem-specific Measures. July 7, 2009 Data Mining: R. Akerkar 21
  22. 22. The K-Means Clustering Method K Means• Given k, the k-means algorithm is implemented in k means four steps: – Partition objects into k nonempty subsets – Compute seed points as the centroids of the clusters of the current partition (the centroid is the center, i.e., mean center i e point, of the cluster) – Assign each object to the cluster with the nearest seed point – Go back to Step 2, stop when no more new assignmentJuly 7, 2009 Data Mining: R. Akerkar 22
  23. 23. Visualization of k-means k means algorithmJuly 7, 2009 Data Mining: R. Akerkar 23
  24. 24. Exercise 2• Apply the K-means algorithm for the following 1-dimensional points (for k=2): 1; g p ( ) ; 2; 3; 4; 6; 7; 8; 9.• Use 1 and 2 as the starting centroids. centroidsJuly 7, 2009 Data Mining: R. Akerkar 24
  25. 25. K – Mean for 2-dimensional 2 dimensional database• Let us consider {x1, x2, x3, x4, x5} with following coordinates as two-dimensional sample for clustering:• x1 = (0, 2), x2 = (0, 0), x3 = (1.5,0), x4 = (5,0), x5 = (5, 2)• Suppose that required number of clusters is 2.• Initially, clusters are formed from random distribution of samples:• C1 = {x1, x2, x4} and C2 = {x3, x5}.July 7, 2009 Data Mining: R. Akerkar 25
  26. 26. Centroid Calculation• Suppose that the given set of N samples in an n-dimensional space has somehow be partitioned into K clusters {C1, C2, …, Ck}• Each Ck has nk samples and each sample is exactly in one cluster.• Therefore,  nk = N, where k = 1, …, K.• The mean vector Mk of cluster Ck is defined as centroid of the cluster, nk Mk = (1/ nk)  i = 1 xik Where xik is the ith sample belonging to cluster Ck.• In our example The centroids for these two clusters are example,• M1 = {(0 + 0 + 5)/3, (2 + 0 + 0)/3} = {1.66, 0.66}• M2 = {( 1.5 + 5)/2, (0 +2)/2} = {3.25, 1.00} ) ( ) } { } July 7, 2009 Data Mining: R. Akerkar 26
  27. 27. The S Th Square-error of th cluster f the l t• The square-error for cluster Ck is the sum of squared Euclidean distances between each sample in Ck and its centroid.• Thi error is called the within-cluster variation. This i ll d th ithi l t i ti ek2 = n k i=1 (xik – Mk)2• Within cluster variations, after initial random distribution of samples, are• e12 = [(0 – 1.66)2 + (2 – 0.66)2] + [(0 – 1.66)2 + (0 – 0.66)2] + [(5 – 1.66)2 + (0 – 0.66)2] = 19.36• e22 = [(1.5 – 3.25)2 + (0 – 1)2] + [( – 3.25)2 + (2 – 1)2] = 8.12 [( ) ( ) [(5 ) ( )July 7, 2009 Data Mining: R. Akerkar 27
  28. 28. Total Square-error• The square error for the entire clustering space containing K clusters is the sum of the within-cluster variations. K i i Ek2 =  k = 1 ek 2• The total square error is E2 = e12 + e22 = 19.36 + 8.12 = 27.48July 7, 2009 Data Mining: R. Akerkar 28
  29. 29. • When we reassign all samples, depending on a minimum distance from centroids M1 and M2, the new redistribution of samples inside clusters will be, be• d(M1, x1) = (1.662 + 1.342)1/2 = 2.14 and d(M2, x1) = 3.40  x1  C1• d(M1, x2) = 1.79 and d(M2, x2) = 3.40  x2  C1 d(M1, x3) = 0.83 and d(M2, x3) = 2.01  x3  C1 d(M1, x4) = 3.41 and d(M2, x4) = 2.01  x4  C2 d(M1, x5) = 3.60 and d(M2, x5) = 2.01  x5  C2 Above calculation is based on Euclidean distance formula, d(xi, xj) = k = 1 (xik – xjk)1/2 m July 7, 2009 Data Mining: R. Akerkar 29
  30. 30. • New Clusters C1 = {x1, x2, x3} and C2 = {x4, x5} have new centroids• M1 = {0.5, 0.67}• M2 = {5.0, 1.0}• The corresponding within-cluster variations and the total square error are,• e12 = 4.17• e22 = 2.00• E2 = 6.17July 7, 2009 Data Mining: R. Akerkar 30
  31. 31. Exercise 3Let the set X consist of the following sample points in 2 dimensional space:X = {(1, 2), (1.5, 2.2), (3, 2.3), (2.5,-1), (0, 1.6), (-1,1.5)}Let c1 = (1.5, 2.5) and c2 = (3, 1) be initial estimates of centroids for X.What are the revised values of c1 and c2 after 1 iteration of k- means clustering (k = 2)?July 7, 2009 Data Mining: R. Akerkar 31
  32. 32. Association Rule DiscoveryJuly 7, 2009 Data Mining: R. Akerkar 32
  33. 33. Associations discovery• Associations discovery uncovers affinities amongst collection of items• Affinities are represented by association rules• Associations discovery is an unsupervised approach to data mining.July 7, 2009 Data Mining: R. Akerkar 33
  34. 34. Association discovery is one of the most common forms of data mining that people closely associate with data mining, namely mining for gold through a vast database. The gold in this ld th h td t b Th ld i thi case is a rule that tells you something about your database that you did not already know, and know were probably unable to explicitly articulateJuly 7, 2009 Data Mining: R. Akerkar 34
  35. 35. Association discovery is done using rule induction which y g basically tells a user how strong a pattern is and how likely it is to happen again. For instance a database of items scanned in a consumer market basket helps finding interesting patterns such as: If bagels are purchased then cream cheese is purchased 90% of the time and this pattern occurs i 3% of all shopping baskets in f ll h i b kYou go tell the data base to go find the rules, the rules that are rules pulled from the database are extracted and ordered to be presented to the user to according to the percentage of times they are correct and how often they apply. Often gets lot of rules and the user almost needs a second pass to find his/her gold nugget. g ggJuly 7, 2009 Data Mining: R. Akerkar 35
  36. 36. Associations• The problem of deriving associations from data – market-basket analysis – The popular algorithms are thus concerned with determining the set of frequent itemsets in a given set of operation databases. – The problem is to compute the frequency of occurrences of each itemset in the database.July 7, 2009 Data Mining: R. Akerkar 36
  37. 37. DefinitionJuly 7, 2009 Data Mining: R. Akerkar 37
  38. 38. Association Rules• Algorithms that obtain association rules from data usually divide the task into two y parts: – find the frequent itemsets and – form the rules from them.July 7, 2009 Data Mining: R. Akerkar 38
  39. 39. Association Rules• The problem of mining association rules can be divided into two sub-problems:July 7, 2009 Data Mining: R. Akerkar 39
  40. 40. a priori AlgorithmJuly 7, 2009 Data Mining: R. Akerkar 40
  41. 41. Exercise 3Suppose that L3 is the list {{a,b,c}, {a,b,d}. {a,c,d}, {b,c,d}. {b,c,w}, {b,c,x}, {p,q,r}, {p,q,s}, {p,q,t}, {p,r,s}, {q,r,s}}Which itemsets are placed in C4 by the join step of the Apriori algorithm? Which are p p g then removed by the prune step?July 7, 2009 Data Mining: R. Akerkar 41
  42. 42. Exercise 4• Given a dataset with four attributes w, x, y and z, each with three values, how many , , y rules can be generated with one term on the right-hand side? gJuly 7, 2009 Data Mining: R. Akerkar 42
  43. 43. References• R. Akerkar and P. Lingras. Building an Intelligent Web: Theory & Practice, Jones & Bartlett, 2008 (In India: Narosa Publishing House, 2009)• U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Mining Press 1996• U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001• J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001 K f• D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001 , July 7, 2009 Data Mining: R. Akerkar 43
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