ELSES PROPERTY ARISm ORE IO Plagiarism Checker Similar Content search Online Plagiarism Paste Original content Here Paste Alternate content Here esting Plagiarism ABC College for Women is one of the most prestigious since the establishment of ABC College for Women and in early Januar institutions of London with a full time enrollment of about 8000 students 12, the University has tried its level best for improvement in Highes Government did variou institution have been shaped by its institutional history, which is spread foreign universities MoU with various national industries and linkages with t years. In 02, the University made all strong foreign universities have been established in the field of Pharmacy sions for the improvement in on. Established in May Electronics, Ent al Science, Fine Arts, Economics and Mass Communication. This is how they made the glorious academic values of University of the Oxford, it was housed in a building on XYZ Road,with his oldest premier post-graduate female institution very nicely ngth of 90 students and then the progress flourished with full shot And College started programs like Electronics, Environmental Science, ts, Economics and Mass Communication, Various national industries and linkages with Foreign Colleges helped a lot c842W:130s:6P:1 C:558W805:3P1 lear Highlight Clear all Import ar Highlight Clear all Occurances Density Matching Limit Case Sensitive Higher Education. Established 1% Scan Density 2.31% Statistics: Plagiarism ABC College 2.31% 30 %/o Duplicate Found! Education, Established in 231% a Export csy 39 Matches Detected Established in May Export相ML for Women s 231% Scan Now 6
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Transaction data: a set of documents A text document data set, each document is treated as a“bag” of keywords doc 1 Student, Teach, School doc2 Student School docs Teach, School, City, Game doc Baseball, Basketball doc5 Basketball, Player, Spectator doc Baseball. Coach. Game, Team doc: Basketball, Team, City, Game
7 Transaction data: a set of documents ◼ A text document data set. Each document is treated as a “bag” of keywords doc1: Student, Teach, School doc2: Student, School doc3: Teach, School, City, Game doc4: Baseball, Basketball doc5: Basketball, Player, Spectator doc6: Baseball, Coach, Game, Team doc7: Basketball, Team, City, Game
The model: rules a transaction t contains x. a set of items (itemset)in / ifX c t An association rule is an implication of the form X→>Y, Where x,Ycl,andX∩Y= An itemset is a set of items + E.g., X=milk, bread, cereal) is an itemset ak-itemset is an itemset with k items E.g., milk, bread, cereal] is a 3-itemset
8 The model: rules ◼ A transaction t contains X, a set of items (itemset) in I, if X t. ◼ An association rule is an implication of the form: X → Y, where X, Y I, and X Y = ◼ An itemset is a set of items. ◆ E.g., X = {milk, bread, cereal} is an itemset. ◼ A k-itemset is an itemset with k items. ◆ E.g., {milk, bread, cereal} is a 3-itemset
Rule strength measures (the transaction data set) if sup gor sup in T Support: The rule holds with support transactions containⅩ∪Y ◆Sp=Pr(x∪Y Confidence. The rule holds in t with confidence conf if conf of tranactions that contain x also contain y conf=Pr(r X) a An association rule is a pattern that states When x occurs. y occurs with certain probability
9 Rule strength measures ◼ Support: The rule holds with support sup in T (the transaction data set) if sup % of transactions contain X Y. ◆ sup = Pr(X Y). ◼ Confidence: The rule holds in T with confidence conf if conf % of tranactions that contain X also contain Y. ◆ conf = Pr(Y | X) ◼ An association rule is a pattern that states when X occurs, Y occurs with certain probability
Support and Confidence Support count: The support count of an itemset X, denoted by X count, in a data set T is the number of transactions in t that contain X assume t has n transactions Then (X∪Y) count support= (X∪) count confidence Xcount
10 Support and Confidence ◼ Support count: The support count of an itemset X, denoted by X.count, in a data set T is the number of transactions in T that contain X. Assume T has n transactions. ◼ Then, n X Y count support ( ). = X count X Y count confidence . ( ). =