Consider the following transactions table: Transaction ID 1 2 3 4 5 6 7 O (A) 5, (B) 3 If the minimum support for the 31 possible item sets is 4/7, then the total number of closed item sets is (A) and the total number of maximal item sets is (B)_______ (A) 5, (B) 2 O(A) 2, (B) 5 (A) 3, (B) 5 Items O I don't know yet {A, C, D} {B, C, E} {A, B, C, E} {B, E} {A, B, C, E} {D, E} {B, C, D)

Consider the following transactions table: Transaction ID 1 2 3 4 5 6 7 O (A) 5, (B) 3 If the minimum support for the 31 possible item sets is 4/7, then the total number of closed item sets is (A) and the total number of maximal item sets is (B)_______ (A) 5, (B) 2 O(A) 2, (B) 5 (A) 3, (B) 5 Items O I don't know yet {A, C, D} {B, C, E} {A, B, C, E} {B, E} {A, B, C, E} {D, E} {B, C, D)

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Passenger (pid, pbithday, pcity)Station (sid, sname, sxcoord, sycoord)CheckIn (pid, departure_sid, arrive_sid, departure_date, arrive_date)departure_sid reference sid in Stationarrive_sid reference sid in Stationpid references pid in Passenger there are 400 million Passenger, 500 stations, and 20 billion checkIn stored in the schema. For simplicity, assume that every attribute is of size 8 bytes, and the size of each tuple is thus 8*x bytes for a table with x attributes, and that the size of a table is simply the size of a tuple multiplied by the number of tuples (i.e., 100% occupancy and no gaps or space for block metadata). Now consider the following queries: SELECT P.pidFROM Passenger p, CheckIn C, Station SWHERE P.pid = C.pidand C.departure_sid = S.sidand S.sname = "LA"and year(P.pbithday) = 2000and date(C.departure_date) = "2015-12-12" question:1. first join P with C, and then join result with S2. first join S with C, and then join result with PWhich way would be faster for this…
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A transaction database contains ten transactions shown below and minimum_support = 30%. Using A-priori, find all frequent itemsets that contain item i. How many frequent itemsets that contain i do we have? TID Items bought { s, u, s, a, n} { s, a, r, a} { s, a, m} 1 2 3 {r, i, c, h, a, r, d} {e,r, i, c} {n, i, c, k} {p, a, t, r, i, c, k} {e, m, i, 1, y} {c,h, a, r, l, e, s} 6. 7 8 9. 10 [lidal
Suppose that you are running manually the Apriori algorithm to find the frequent itemsets in a given transaction database. Currently, you have determined the candidate set of 2-itemsets, C2, and the corresponding support count of each candidate 2-itemset. Assuming that the minimum support count is 3, which candidate 2-itemsets in C₂ would be frequent 2- itemsets? Select all that apply. {I1, I2), support count = 1 {I₁, I3}, support count 3 {I₁, I4}, support count = 3 {I2, I3), support count {I2, I4}, support count = 1 {I3, I4}, support count = 4 = 2
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