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34 Pages

### assoc-rules1-3

Course: CS 345, Fall 2001
School: Stanford
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Word Count: 1260

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Rules Market Association Baskets Frequent Itemsets A-priori Algorithm 1 The Market-Basket Model A large set of items, e.g., things sold in a supermarket. A large set of baskets, each of which is a small set of the items, e.g., the things one customer buys on one day. 2 Support Simplest question: find sets of items that appear &quot;frequently&quot; in the baskets. Support for itemset I = the number of...

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Stanford - CS - 345
Association RulesMarket Baskets Frequent Itemsets Apriori Algorithm1The MarketBasket Modelx A large set of items, e.g., things sold in a supermarket. x A large set of baskets, each of which is a small set of the items, e.g., the things one customer bu
Stanford - CS - 345
Association RulesMarket Baskets Frequent Itemsets A-Priori Algorithm1The Market-Basket ModelA large set of items, e.g., things sold in a supermarket. A large set of baskets, each of which is a small set of the items, e.g., the things one customer buys
Stanford - CS - 345
Association RulesMarket Baskets Frequent Itemsets APriori Algorithm1The MarketBasket Modelx A large set of items, e.g., things sold in a supermarket. x A large set of baskets, each of which is a small set of the items, e.g., the things one customer bu
Stanford - CS - 345
Improvements to A-PrioriPark-Chen-Yu Algorithm Multistage Algorithm Approximate Algorithms Compacting Results1PCY AlgorithmHash-based improvement to A-Priori. During Pass 1 of A-priori, most memory is idle. Use that memory to keep counts of buckets in
Stanford - CS - 345
Improvements to APrioriParkChenYu Algorithm Multistage Algorithm Approximate Algorithms Compacting Results1PCY Algorithmx Hashbased improvement to APriori. x During Pass 1 of Apriori, most memory is idle. x Use that memory to keep counts of buckets in
Stanford - CS - 345
Improvements to APrioriParkChenYu Algorithm Multistage Algorithm Approximate Algorithms Compacting Results1PCY Algorithmx Hashbased improvement to APriori. x During Pass 1 of Apriori, most memory is idle. x Use that memory to keep counts of buckets in
Stanford - CS - 345
Improvements to APrioriBloom Filters ParkChenYu Algorithm Multistage Algorithm Approximate Algorithms Compacting Results1Aside: HashBased Filteringx Simple problem: I have a set S of one billion strings of length 10. x I want to scan a larger file F o
Stanford - CS - 345
SQL/MRPeter Pawlowski Member of Technical Staff January 16, 2009ASTER BACKGROUND2Our Founders3 PhD students from Stanford C.S. Cool ideas. . but no funding, no product, no clients!OK, they had \$ 10,000.3Our Product: nCluster A massively scalable
Stanford - CS - 345
Clustering AlgorithmsApplications Hierarchical Clustering k Means Algorithms CURE Algorithm1The Problem of Clusteringx Given a set of points, with a notion of distance between points, group the points into some number of clusters, so that members of a
Stanford - CS - 345
Clustering PreliminariesApplications Euclidean/Non-Euclidean Spaces Distance Measures1The Problem of ClusteringGiven a set of points, with a notion of distance between points, group the points into some number of clusters, so that members of a cluster
Stanford - CS - 345
Clustering PreliminariesApplications Euclidean/NonEuclidean Spaces Distance Measures1The Problem of Clusteringx Given a set of points, with a notion of distance between points, group the points into some number of clusters, so that members of a cluste
Stanford - CS - 345
Clustering PreliminariesApplications Euclidean/Non-Euclidean Spaces Distance Measures1The Problem of ClusteringGiven a set of points, with a notion of distance between points, group the points into some number of clusters, so that members of a cluster
Stanford - CS - 345
Clustering PreliminariesApplications Euclidean/NonEuclidean Spaces Distance Measures1The Problem of Clusteringx Given a set of points, with a notion of distance between points, group the points into some number of clusters, so that members of a cluste
Stanford - CS - 345
Clustering AlgorithmsHierarchical Clustering k -Means Algorithms CURE Algorithm1Methods of ClusteringHierarchical (Agglomerative):Initially, each point in cluster by itself. Repeatedly combine the two &quot;nearest&quot; clusters into one.Point Assignment:Ma
Stanford - CS - 345
Clustering AlgorithmsHierarchical Clustering k Means Algorithms CURE Algorithm1Methods of Clusteringx Hierarchical (Agglomerative): Initially, each point in cluster by itself. Repeatedly combine the two &quot;nearest&quot; clusters into one. Maintain a set of
Stanford - CS - 345
Clustering AlgorithmsHierarchical Clustering k -Means Algorithms CURE Algorithm1Methods of ClusteringHierarchical (Agglomerative):Initially, each point in cluster by itself. Repeatedly combine the two nearest clusters into one.Point Assignment:Main
Stanford - CS - 345
Clustering AlgorithmsHierarchical Clustering k Means Algorithms CURE Algorithm1Methods of Clusteringx Hierarchical (Agglomerative): Initially, each point in cluster by itself. Repeatedly combine the two &quot;nearest&quot; clusters into one. Maintain a set of
Stanford - CS - 345
CS345 Data Mining Crawling the Web Web Crawling BasicsStart with a &quot;seed set&quot; of tovisit urlsget next url get page extract urlsto visit urlsWebvisited urlsweb pagesCrawling Issues Load on web servers Insufficient resources to crawl entire web
Stanford - CS - 345
Problem 1:a) True Consider visiting the rows in the permuted order. The first time you see a one in any of the two columns, the column C1 \/ C2 will also have a one. Consequently, the first (minimum) row number which corresponds to the min hash value for
Stanford - CS - 345
Locality-Sensitive HashingBasic Technique Hamming-LSH Applications1Finding Similar PairsSuppose we have in main memory data representing a large number of objects.May be the objects themselves (e.g., summaries of faces). May be signatures as in minha
Stanford - CS - 345
LocalitySensitive HashingBasic Technique HammingLSH Applications1Finding Similar Pairsx Suppose we have in main memory data representing a large number of objects. May be the objects themselves (e.g., summaries of faces). May be signatures as in minh
Stanford - CS - 345
Finding Similar PairsDivideComputeMerge LocalitySensitive Hashing Applications1Finding Similar Pairsx Suppose we have in main memory data representing a large number of objects. May be the objects themselves (e.g., summaries of faces). May be signatu
Stanford - CS - 345
Mining Data StreamsThe Stream Model Sliding Windows Counting 1s1The Stream ModelData enters at a rapid rate from one or more input ports. The system cannot store the entire stream. How do you make critical calculations about the stream using a limited
Stanford - CS - 345
Mining Data StreamsThe Stream Model Sliding Windows Counting 1's1The Stream Modelx Data enters at a rapid rate from one or more input ports. x The system cannot store the entire stream. x How do you make critical calculations about the stream using a
Stanford - CS - 345
Mining Data StreamsThe Stream Model Sliding Windows Counting 1's1Data Management Versus Stream ManagementIn a DBMS, input is under the control of the programmer.SQL INSERT commands or bulk loaders.Stream Management is important when the input rate i
Stanford - CS - 345
Mining Data StreamsThe Stream Model Sliding Windows Counting 1's1Data Management Versus Stream Managementx In a DBMS, input is under the control of the programmer. x Stream Management is important when the input rate is controlled externally. Example
Stanford - CS - 345
More Stream-MiningCounting How Many Elements Computing Moments1Counting Distinct ElementsProblem: a data stream consists of elements chosen from a set of size n. Maintain a count of the number of distinct elements seen so far. Obvious approach: mainta
Stanford - CS - 345
More StreamMiningCounting How Many Elements Computing &quot;Moments&quot;1Counting Distinct Elementsx Problem: a data stream consists of elements chosen from a set of size n. Maintain a count of the number of distinct elements seen so far. x Obvious approach: m
Stanford - CS - 345
More Stream-MiningCounting Distinct Elements Computing &quot;Moments&quot; Frequent Itemsets Elephants and Troops Exponentially Decaying Windows1Counting Distinct ElementsProblem: a data stream consists of elements chosen from a set of size n. Maintain a count
Stanford - CS - 345
More StreamMiningCounting Distinct Elements Computing &quot;Moments&quot; Frequent Itemsets Elephants and Troops Exponentially Decaying Windows1Counting Distinct Elementsx Problem: a data stream consists of elements chosen from a set of size n. Maintain a count
Stanford - CS - 345
Still More Stream-MiningFrequent Itemsets Elephants and Troops Exponentially Decaying Windows1Counting ItemsProblem: given a stream, which items appear more than s times in the window? Possible solution: think of the stream of baskets as one binary st
Stanford - CS - 345
Still More StreamMiningFrequent Itemsets Elephants and Troops Exponentially Decaying Windows1Counting Itemsx Problem: given a stream, which items appear more than s times in the window? x Possible solution: think of the stream of baskets as one binary
Stanford - CS - 345
Stream ClusteringExtension of DGIM to More Complex Problems1Clustering a StreamAssume points enter in a stream. Maintain a sliding window of points. Queries ask for clusters of points within some suffix of the window. Important issue: where are the cl
Stanford - CS - 345
Stream ClusteringExtension of DGIM to More Complex Problems1Clustering a Streamx Assume points enter in a stream. x Maintain a sliding window of points. x Queries ask for clusters of points within some suffix of the window. x Important issue: where ar
Stanford - CS - 345
CS345 Data MiningIntroductions What Is It? Cultures of Data Mining1Course Staffx Instructors: Anand Rajaraman Jeff Ullman Robbie Yanx TA:2Requirementsx Homework (Gradiance and other) 20% x Project 40% x Final Exam 40% Gradiance class code BB8F69
Stanford - CS - 345
CS345 - Data MiningIntroductions What Is It? Cultures of Data Mining1Course StaffInstructors:Anand Rajaraman Jeff UllmanTA:Jeff Klingner2RequirementsHomework (Gradiance and other) 20%Gradiance class code DD984360Project 40% Final Exam 40%3Pr
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CS345 Data MiningIntroductions What Is It? Cultures of Data Mining1Course Staffx Instructors: Anand Rajaraman Jeff Ullman Jeff Klingnerx TA:2Requirementsx Homework (Gradiance and other) 20% x Project 40% x Final Exam 40% Gradiance class code DD9
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CS345 - Data MiningCourse Introduction Varieties of Data Mining Bonferroni's Principle1Course StaffInstructors:Anand Rajaraman Jeff UllmanTA:Babak Pahlavan2RequirementsHomework (Gradiance and other) 20%Gradiance class code B0E9AA66 Note URL for
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CS345A: Data Mining on the WebCourse Introduction Issues in Data Mining Bonferroni's Principle1Course Staffx Instructors: Anand Rajaraman Jeff Ullman Babak Pahlavanx TA:2Requirementsx Homework (Gradiance and other) 20% Gradiance class code B0E9A
Stanford - CS - 345
CS345A: Data Mining on the WebCourse Introduction Issues in Data Mining Bonferroni's Principle1Course Staffx Instructors: Anand Rajaraman Jeff Ullmanx Reach us as cs345awin0809staff @ lists.stanford.edu. x More info on www.stanford.edu/class/cs345a.
Stanford - CS - 345
Generalizing MapReduceThe Computational Model MapReduceLike Algorithms Computing Joins1Overviewx There is a new computing environment available: x Mapreduce allows us to exploit this environment easily. x But not everything is mapreduce. x What else c
Stanford - CS - 345
CS 345A Data MiningMapReduceSingle-node architectureCPU Machine Learning, Statistics Memory &quot;Classical&quot; Data Mining DiskCommodity ClustersWeb data sets can be very largeTens to hundreds of terabytesCannot mine on a single server (why?) Standard arc
Stanford - CS - 345
CS 345A Data MiningMapReduce Singlenode architectureCPU Machine Learning, Statistics Memory &quot;Classical&quot; Data Mining DiskCommodity ClustersWeb data sets can be very large Cannot mine on a single server (why?) Standard architecture emerging: Te
Stanford - CS - 345
CS 345A Data MiningMapReduceSingle-node architectureCPU Machine Learning, Statistics Memory Classical Data Mining DiskCommodity ClustersWeb data sets can be very largeTens to hundreds of terabytesCannot mine on a single server (why?) Standard archi
Stanford - CS - 345
CS 345A Data MiningMapReduce Singlenode architectureCPU Machine Learning, Statistics Memory &quot;Classical&quot; Data Mining DiskCommodity ClustersWeb data sets can be very large Cannot mine on a single server (why?) Standard architecture emerging: Te
Stanford - CS - 345
Near-Neighbor SearchApplications Matrix Formulation Minhashing1Example Application: Face RecognitionWe have a database of (say) 1 million face images. We want to find the most similar images in the database. Represent faces by (relatively) invariant v
Stanford - CS - 345
NearNeighbor SearchApplications Matrix Formulation Minhashing1Example Application: Face Recognitionx We have a database of (say) 1 million face images. x We want to find the most similar images in the database. x Represent faces by (relatively) invari
Stanford - CS - 345
Near-Neighbor SearchApplications Matrix Formulation Minhashing1Example Problem - Face RecognitionWe have a database of (say) 1 million face images. We are given a new image and want to find the most similar images in the database. Represent faces by (
Stanford - CS - 345
NearNeighbor SearchApplications Matrix Formulation Minhashing1Example Problem Face Recognitionx We have a database of (say) 1 million face images. x We are given a new image and want to find the most similar images in the database. x Represent faces b
Stanford - CS - 345
What is Database Theory?A collection of studies, often connected to the relational model of data. Restricted forms of logic, between SQL and full rst-order. Dependency theory: generalizing functional dependencies. Conjunctive queries CQ's: useful, decida
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CS345 Data MiningLink Analysis Algorithms Page Rank Anand Rajaraman, Jeffrey D. UllmanLink Analysis Algorithms Page Rank Hubs and Authorities TopicSpecific Page Rank Spam Detection Algorithms Other interesting topics we won't cover Detecting dup
Stanford - CS - 345
Link Analysis AlgorithmsCS345 Data MiningLink Analysis Algorithms Page RankPage Rank Hubs and Authorities Topic-Specific Page Rank Spam Detection Algorithms Other interesting topics we wont coverDetecting duplicates and mirrors Mining for communities
Stanford - CS - 345
CS345 Data MiningLink Analysis Algorithms Page Rank Anand Rajaraman, Jeffrey D. UllmanLink Analysis Algorithms Page Rank Hubs and Authorities TopicSpecific Page Rank Spam Detection Algorithms Other interesting topics we won't cover Detecting dup
Stanford - CS - 345
CS345 Data MiningLink Analysis Algorithms Page RankAnand Rajaraman, Jeffrey D. UllmanLink Analysis AlgorithmsPage Rank Hubs and Authorities Topic-Specific Page Rank Spam Detection Algorithms Other interesting topics we wont coverDetecting duplicates
Stanford - CS - 345
CS345 Data MiningLink Analysis Algorithms Page RankAnand Rajaraman, Jeffrey D. UllmanLink Analysis AlgorithmsPage Rank Hubs and Authorities Topic-Specific Page Rank Spam Detection Algorithms Other interesting topics we won't coverDetecting duplicates
Stanford - CS - 345
CS345 Data MiningLink Analysis Algorithms Page Rank Anand Rajaraman, Jeffrey D. UllmanLink Analysis Algorithms Page Rank Hubs and Authorities TopicSpecific Page Rank Spam Detection Algorithms Other interesting topics we won't cover Detecting dup
Stanford - CS - 345
TopicsCS345 Data MiningLink Analysis 2 Page Rank VariantsThis lectureMany-walkers model Tricks for speeding convergence Topic-Specific Page RankAnand Rajaraman, Jeffrey D. UllmanRandom walk interpretationAt time 0, pick a page on the web uniformly
Stanford - CS - 345
CS345 Data MiningLink Analysis 2 Page Rank Variants Anand Rajaraman, Jeffrey D. UllmanTopicsThis lecture Manywalkers model Tricks for speeding convergence TopicSpecific Page RankRandom walk interpretation At time 0, pick a page on the web unif
Stanford - CS - 345
CS345 Data MiningRecommendation SystemsAnand Rajaraman, Jeffrey D. UllmanRecommendationsSearchRecommendationsItemsProducts, web sites, blogs, news items, The Long TailSource: Chris Anderson (2004)From scarcity to abundanceShelf space is a scarc
Stanford - CS - 345
CS345 Data MiningRecommendation Systems Netflix Challenge Course Projects Anand Rajaraman, Jeffrey D. UllmanRecommendations SearchRecommendationsItemsProducts, web sites, blogs, news items, .From scarcity to abundanceShelf space is a scarce com
Stanford - CS - 345
CS345 Data MiningRecommendation Systems Anand Rajaraman, Jeffrey D. UllmanRecommendations SearchRecommendationsItemsProducts, web sites, blogs, news items, .The Long TailSource: Chris Anderson (2004)From scarcity to abundanceShelf space is a