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Data Mining

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Data Mining (DM)/ Knowledge Discovery in Databases (KDD) “The nontrivial extraction of implicit, previously unknown, and potentially useful information from data” [Frawley et al, 1992]

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Need for Data Mining u Increased ability to generate data u Remote sensors and satellites u Bar codes for commercial products u Computerization of businesses

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Need for Data Mining u Increased ability to store data u Media: bigger magnetic disks, CD-ROMs u Better database management systems u Data warehousing technology

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Need for Data Mining u Examples u Wal-Mart records 20,000,000 transactions/day u Healthcare transactions yield multi-GB databases u Mobil Oil exploration storing 100 terabytes u Human Genome Project, multi-GBs and increasing u Astronomical object catalogs, terabytes of images u NASA EOS, 1 terabyte/day

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Something for Everyone u Bell Atlantic u MCI u Land’s End u Visa u Bank of New York u FedEx

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Market Analysis and Management u Customer profiling u Data mining can tell you what types of customers buy what products (clustering or classification) or what products are often bought together (association rules). u Identifying customer requirements u Discover relationship between personal characteristics and probability of purchase u Discover correlations between purchases

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Fraud Detection and Management u Applications: u Widely used in health care, retail, credit card services, telecommunications, etc. u Approach: u Use historical data to build models of fraudulent behavior and use data mining to help identify similar instances. u Examples: u Auto Insurance u Money Laundering u Medical Insurance

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IBM Advertisement

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DM step in KDD Process

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Database AI Statistics Data Mining Hardware

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Mining Association Rules

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u Assocation rule mining: u Finding associations or correlations among a set of items or objects in transaction databases, relational databases, and data warehouses. u Applications: u Basket data analysis, cross-marketing, catalog design, loss- leader analysis, clustering, etc. u Examples: Rule form: “Body ead [support, confidence]”. Buys=Diapers Buys=Beer [0.5%, 60%] Major=CS ^ Class=DataMining Grade=A [1%, 75%]

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Rule Measures: Support and Confidence u Find all the rules X & Y Z with minimum confidence and support u support, s, probability that a transaction contains {X, Y, Z} u confidence, c, conditional probability that a transaction having {X, Y} also contains Z. For minimum support 50%, minimum confidence 50%: A C (50%, 66.6%) C A (50%, 100%) Customer buys diaper Customer buys both Customer buys beer

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Association Rule u Given u Set of items I = {i 1, i 2,.., i m } u Set of transactions D u Each transaction T in D is a set of items u An association rule is an implication u X and Y are itemsets, u Rule meets minimum confidence c (c% of transactions in D which contain X contain Y) u A minimum support s is also met XYXc/ DYXs/

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Mining Strong Association Rules in Transaction DBs u Measurement of rule strength in a transaction DB. A B [support, confidence] support = Prob( B) = confidence = Prob(B|A) = u We are often interested in only strong associations, i.e. support min_sup and confidence min_conf. u Examples. milk bread [5%, 60%]. tire auto_accessories auto_services [2%, 80%]. #_of_trans_containing_all_the_items_in A B total_#_of_trans #_of_trans_that_contain_both A and B #_of_trans_containing A

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Methods for Mining Associations u Apriori u Partition Technique: u Sampling technique u Anti-Skew u Multi-level or generalized association u Constraint-based or query-based association

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Apriori (Levelwise) u Scan database multiple times u For ith scan, find all large itemsets of size i with min support u Use the large itemsets from scan i as input to scan i+1 u Create candidates, subsets of size i+1 which contain only large itemsets as subsets u Notation: Large k-itemset, L k Set of candidate large itemsets of size k, C k u Note: If {A,B} is not a large itemset, then no superset of it can be either.

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Mining Association Rules -- Example For rule A C: support = support({A, C}) = 50% confidence = support({A, C})/support({A}) = 66.6% Apriori principle: Any subset of a frequent itemset must be frequent. Min. support 50% Min. confidence 50%

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L 1 = {(A, 3), (B, 2), (C, 2), (D, 1), (E, 1), (F, 1)} Minsup = 0.25, Minconf = 0.5 C 2 = {(A,B), (A,C), (A,D), (A,E), (A,F), (B,C),.., (E,F)} L 2 = {(A,B, 1), (A,C, 2), (A,D, 1), (B,C, 1), (B,E, 1), (B,F, 1), (E,F, 1)} C 3 = {(A,B,C), (A,B,D), (A,C,D), (B,C,E), (B,C,F), (B,E,F)} L 3 = {(A,B,C, 1), (B,E,F, 1)} C 4 = {}, L4 = {}, End of program Possible Rules A=>B (c=.33,s=1), B=>A (c=.5,s=1), A=>C (c=.67,s=2), C=>A (c=1.0,s=2) A=>D (c=.33,s=1), D=>A (c=1.0,s=1), B=>C (c=.5,s=1), C=>B (.5,s=1), B=>E (c=.5,s=1), E=>B(c=1,s=1), B=>F (c=.5,s=1), F=>B(c=1,s=1) A=>B&C (c=.33,s=1), B=>A&C (c=.5,s=1), C=>A&B (c=.5,s=1), A&B=>C(c=1,s=1), A&C=>B (c=.5,s=1), B&C=>A (c=1,s=1), B=>E&F (c=.5,s=1), E=>B&F(c=1,s=1), F=>B&E (c=1,s=1), B&E=>F (c=1,s=1), B&F=>E(c=1,s=1), E&F=>B (c=1,s=1)

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Example

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Partitioning u Requires only two passes through external database u Divide database into n partitions, each fits in main memory u Scan 1: Process one partition in memory at a time, finding local large itemsets u Candidate large itemsets are the union of all local large itemsets (superset of actual large itemsets, contains false +) u Scan 2: Calculate support, determine actual large itemsets u If data is skewed, partitioning may not work well. The chance that a local large itemset is a global large itemset may be small.

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Partitioning u Will any large itemsets be missed? u If l Li, then t1(l)/t1 < MS & t2(l)/t2 < MS & … & tn(l)/tn < MS thus t1(l) + t2(l) + … + tn(l) < MS * (t1 + t2 + … + tn)

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How do run times compare?

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PlayTennis Training Examples DayOutlookTemperatureHumidityWindPlayTennis D1SunnyHotHighWeakNo D2SunnyHotHighStrongNo D3OvercastHotHighWeakYes D4RainMildHighWeakYes D5RainCoolNormalWeakYes D6RainCoolNormalStrongNo D7OvercastCoolNormalStrongYes D8SunnyMildHighWeakNo D9SunnyCoolNormalWeakYes D10RainMildNormalWeakYes D11SunnyMildNormalStrongYes D12OvercastMildHighStrongYes D13OvercastHotNormalWeakYes D14RainMildHighStrongNo

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Association Rule Visualization: DBMiner

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DBMiner

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Association Rule Graph

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Clementine (UK, bought by SPSS) The Web Node shows the strength of associations in the data - i.e. how often field values coincide

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Multi-Level Association u A descendant of an infrequent itemset cannot be frequent u A transaction database can be encoded by dimensions and levels Food bread milk skim SunsetFraser 2%white wheat

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Encoding Hierarchical Information in Transaction Database l A taxonomy for the relevant data items l Conversion of bar_code into generalized_item_id. food milk 2%chocolate...... DairylandForemost bread old MillsWonder...

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Mining Surprising Temporal Patterns u Find prevalent rules that hold over large fractions of data u Useful for promotions and store arrangement u Intensively researched 1990 Milk and cereal sell together! Chakrabarti et al

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Prevalent != Interesting u Analysts already know about prevalent rules u Interesting rules are those that deviate from prior expectation u Mining’s payoff is in finding surprising phenomena 1995 1998 Milk and cereal sell together! Zzzz... Milk and cereal sell together!

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Association Rules - Strengths & Weaknesses u Strengths u Understandable and easy to use u Useful u Weaknesses u Brute force methods can be expensive (memory and time) u Apriori is O(CD), where C = sum of sizes of candidates (2 n possible, n = #items) D = size of database u Association does not necessarily imply correlation u Validation? u Maintenance?

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Clustering

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Clustering u Group similar items together u Example: sorting laundry u Similar items may have important attributes / functionality in common u Group customers together with similar interests and spending patterns u Form of unsupervised learning u Cluster objects into classes using rule: u Maximize intraclass similarity, minimize interclass similarity

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Clustering Techniques u Partition u Enumerate all partitions u Score by some criteria u K means u Hierarchical u Model based u Hypothesize model for each cluster u Find model that best fits data u AutoClass, Cobweb

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Clustering Goal u Suppose you transmit coordinates of points drawn randomly from this dataset u Only allowed 2 bits/point u What encoder/decoder will lose least information?

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Idea One u Break into grid u Decode each bit- pair as middle of each grid cell 0001 1110

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Idea Two u Break into grid u Decode each bit- pair as centroid of all data in the grid cell 00 01 11 10

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K Means Clustering 1. Ask user how many clusters (e.g., k=5)

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K Means Clustering 1. Ask user how many clusters (e.g., k=5) 2. Randomly guess k cluster center locations

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K Means Clustering 1. Ask user how many clusters (e.g., k=5) 2. Randomly guess k cluster center locations 3. Each data point finds closest center

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K Means Clustering 1. Ask user how many clusters (e.g., k=5) 2. Randomly guess k cluster center locations 3. Each data point finds closest center 4. Each cluster finds new centroid of its points

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K Means Clustering 1. Ask user how many clusters (e.g., k=5) 2. Randomly guess k cluster center locations 3. Each data point finds closest center 4. Each cluster finds new centroid of its points 5. Repeat until…

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K Means Issues u Computationally efficient u Initialization u Termination condition u Distance measure u What should k be?

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Hierarchical Clustering 1. Each point is its own cluster

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Hierarchical Clustering 1. Each point is its own cluster 2. Find most similar pair of clusters

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Hierarchical Clustering 1. Each point is its own cluster 2. Find most similar pair of clusters 3. Merge it into a parent cluster

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Hierarchical Clustering 1. Each point is its own cluster 2. Find most similar pair of clusters 3. Merge it into a parent cluster 4. Repeat

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Hierarchical Clustering Issues u This was bottom-up clustering (agglomerative clustering) u Can also perform top-down clustering (divisive clustering) u Define similarity between clusters u What is stopping criteria?

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Cluster Visualization: IBM u One row per cluster (# is % size) u Charts show fields, ordered by influence u Pie: outer ring dist for whole, inner ring for cluster u Bar: solid bar for cluster, transparent bar for whole

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Structure-based Brushing u User selects region of interest with magenta triangle u User selects level of detail with colored polyline

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Spatial Visualization: GeoMiner

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Spatial Associations FIND SPATIAL ASSOCIATION RULE DESCRIBING "Golf Course" FROM Washington_Golf_courses, Washington WHERE CLOSE_TO(Washington_Golf_courses.Obj, Washington.Obj, "3 km") AND Washington.CFCC <> "D81" IN RELEVANCE TO Washington_Golf_courses.Obj, Washington.Obj, CFCC SET SUPPORT THRESHOLD 0.5

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Spatial Clustering u How can we cluster points? u What are the distinct features of the clusters? There are more customers with university degrees in clusters located in the West. Thus, we can use different marketing strategies!

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Partek

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Conclusions

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