**All**answer texts must be computer generated. You can draw the diagrams manually. But they should be clear and neat. diagrams).- The hand-in version must be ordered correctly and stapled in the top left corner.
- The hand-in version must include a header page indicating: student name, student number, user id, course number and assignment number.

Transaction ID |
List of Item ID's |

T1 |
A, B, E |

T2 |
B, C, D |

T3 |
B, D, E |

T4 |
C, D, E |

T5 |
B, C, D, E |

T6 |
B, C, E |

Let the min_support = 20% and min_conf = 60%. In this question, we are considering the Apriori algorithm and two of its variations. They are:

- General Apriori algorithm.
- Hash-based Apriori algorithm (Suppose order(
*A*)=1, order(*B*)=2, order(*C*)=3, order(*D*)=4, order(*E*)=5. The hashing function used is hash(x,y) = (order(x) * 10 + order(y)) mod 7, e.g. hash(A, B) = 5). - Partitioning-based Apriori algorithm (Suppose the above transaction
database is divided into two partitions. Transactions
*T1*,*T2*, and*T3*are in one partition while transactions*T4*,*T5*, and*T6*are in the other).

buys(X, Y) => buys(X, "E") -- [s, c]

You can print out the data sheet for
the concept hierarchies and the relation *R* which are used in this
question.

Let the attribute thresholds (denoted as *T(attribute)*) be: *T(major)
= 3*, *T(status) = 2*, *T(age) = 2*, *T(nationality) = 2*, and
*T(gpa) = 3*.

- Derive a characteristic rule for
*R*. - Let the attribute thresholds be the same as above. Derive a discriminant
rule which contrasts
*applied_science*vs.*arts*students.