# COMP5331 Knowledge Discovery in Databases

1/6
COMP5331 Knowledge Discovery in Databases (Fall Semester 2019)
Homework 2
Full Mark: 100 Marks
Coupon Instructions:
1. You can use a coupon to waive any question you want and obtain full marks for this question.
2. You can waive at most one question in each assignment.
3. You can also answer the question you will waive. We will also mark it but will give full marks to this
question.
4. The coupon is non-transferrable. That is, the coupon with a unique ID can be used only by the student
who obtained it in class.
5. Please staple the coupon to the submitted assignment.
6. Please write down the question no. you want to waive on the coupon.
Q1 [20 Marks]
Consider the density-based subspace clustering. The size of a subspace is defined to be the total number of
dimensions for this subspace. For example, subspace {A, B} is of size 2. For each single dimension, the
number of grid units is fixed to a constant c where c is a positive integer greater than 1.
(a) In class, we learnt that the major idea in the KL-transform is to transform the original coordinate system
to a new coordinate system such that we could find clusters in subspaces from the new coordinate system.
Suppose that we use the KL-transform to transform all data points from the original coordinate system to
a new coordinate system (without using Step 6 of the KL-transform (i.e., choosing a subset of attribute
values)). Then, all points are now represented in the new coordinate system. Based on the new coordinate
system, we adopt the density-based subspace clustering to find clusters in some subspaces. Is it always
true that the total number of grid units involved in all clusters based on the new coordinate system is
smaller than that based on the original coordinate system? If yes, please give some justifications without
any formal proof. If no, similarly, please give some counter examples for illustration.
(b) When the size of the subspace is larger, it is less likely that a grid unit with respect to the subspace is
(c) In order to overcome the weakness described in (b), instead of setting a fixed density threshold for the
subspace of any size, we use a smaller density threshold for the subspace of larger size. Specifically, let
Ti be the density threshold for the subspace of size i. If i < j, then Ti > Tj. Let Condition 1 be “Ti > Tj for
any i < j”.
Let Condition 2 be “for any i and j, Ti = Tj”. We know that if Condition 2 is satisfied, then the original
Apriori-like algorithm studied in class can find all subspaces containing dense units.
(i) Under Condition 1, is it always true that we can still adopt the Apriori-like algorithm? If yes, please
(ii) Suppose that we modify Condition 1 to the following form. Let Condition 1 be “Ti = cTi+1 for each
positive integer i”. Assume that we adopt this new form of Condition 1. Under this new form of
Condition 1, is it always true that we can still adopt the Apriori-like algorithm? If yes, please
2/6
Q2 [20 Marks]
(a) Consider a set P containing the following four 2-dimensional data points.
a:(6, 6), b:(8, 8), c:(5, 9), d:(9, 5)
We can make use of the KL-Transform to find a transformed subspace containing a cluster. Let L be the
total number of dimensions in the original space and K be the total number of dimensions in the projected
subspace. Please illustrate the KL-transform technique with the above example when L=2 and K=1.
(b) Consider a set Q containing the following four 2-dimensional data points.
e:(5, 5), f:(9, 9), g:(3, 11), h:(11, 3)
(i) Let p = (xp, yp) be a point in P and q = (xq, yq) be a point in Q. In fact, we could express xq in a linear
form involving xp such that xq = α . xp + β where α and β are 2 real numbers. Similarly, we could
express yq in the same linear form involving yp. Please write down the values of α and β.
(ii) Similar to Part (a), we want to make use of the KL-Transform to find a transformed subspace
containing a cluster for the set Q where L = 2 and K = 1. One “straightforward” or “naïve” method
is to use the same method in Part (a) to obtain the answer. Is it possible to make use of the result in
Part (a) and the result in Part (b)(i) to obtain the answer very quickly? If yes, please explain briefly
and give the answer. There is no need to give a formal proof. A brief description it accepted. If no,
please give an explanation briefly. In this case, derive the answer by using the method in Part (a).
(c) Consider Part (a). It is independent of Part (b). In Part (a), we know that there are 4 points.
Suppose that we have 4 additional points which are identical to the original 4 points. That is, we have
the following 4 additional points. Totally, we have 8 data points.
(6, 6), (8, 8), (5, 9), (9, 5)
One “straightforward” or “naïve” method is to use the same method in Part (a) to obtain the answer. Is it
possible to make use of the result in Part (a) to obtain the answer very quickly? If yes, please explain
briefly and give the answer. There is no need to give a formal proof. A brief description it accepted. If no,
please give an explanation briefly. In this case, derive the answer by using the method in Part (a).
(d) Consider two random variables X and Y with the following probabilistic table.

 X \ Y 1 2 3 1 0 1/8 1/8 2 1/2 0 1/8 3 1/8 0 0

(i) Calculate the conditional entropy of H(X|Y) by using the original definition of the conditional
entropy.
(ii) Calculate H(X|Y) as
– xA yB p(x, y) log p(x|y)
where A = {1, 2, 3} and B = {1, 2, 3}.
3/6
Q3 [20 Marks]
The following shows a history of PhD students with their numbers of published papers, their ages and their
majors. We also indicate whether they become professors or not after their PhD graduation in the last column.
Note that the first column “No.” is for us to refer the record number only.

 No. NoOfPapers Age Major Become_Professor 1 enough young ComputerScience yes 2 many young ComputerScience yes 3 many old CivilEngineering yes 4 many old DataScience yes 5 few young CivilEngineering no 6 many young ComputerScience no 7 few old DataScience no 8 few old ComputerScience no

(a) We want to train a C4.5 decision tree classifier to predict whether a PhD student will become a professor
or not. We define the value of attribute Become_Professor to be the label of a record.
(i) Please find a C4.5 decision tree according to the above example. In the decision tree, whenever
we process (1) a node containing at least 80% records with the same label or (2) a node containing
at most 2 records, we stop to process this node for splitting.
(ii) Consider a young PhD student majoring in computer science who published many papers. Please
estimate the probability that this PhD student will become a professor.
(b) Let X be the set of attributes involved in the decision tree found in Part (a). Person A said that we just
need to consider all attributes in X only to determine whether a student will become a professor. Person
B said that we should also consider attributes outside X (in addition to attributes in X) to determine
whether a student will become a professor.
(i) Please give a possible reason why Person A said in this way.
(ii) Please give a possible reason why Person B said in this way.
(iii) Which Person (Person A or Person B) is more unreasonable in general?
(c) What is the difference between the C4.5 decision tree and the ID3 decision tree? Why is there a difference?
4/6
Q4 [20 Marks]
We have the following Bayesian Belief Network.
Suppose that there is a new patient. We know that
(1) he has acute pancreatitis
(2) he has pneumonia
(3) his result of white blood cell is low
We would like to know whether he is likely to have systemic inflammation reaction.

 Acute Pancreatitis Pneumonia White Blood Cell Systemic Inflammation Reaction Yes Yes Low ?

(a) Please use Bayesian Belief Network classifier with the use of Bayesian Belief Network to predict whether
he is likely to have systemic inflammation reaction.
(b) Although Bayesian Belief Network classifier does not have an independent assumption among all
attributes (compared with the naïve Bayesian classifier), what are the disadvantages of this classifier?
AcutePancreatitis (AP) Pneumonia (P)
SystemicInflammationReaction (SIR)
WhiteBloodCell (WBC)
AP = Yes
0.3
P = Yes
0.6
SIR = Yes
AP = Yes
P = Yes 0.7
AP = Yes
P = No 0.45
AP = No
P = Yes 0.55
AP = No
P = No 0.2
WBC = High
SIR = Yes 0.6
SIR = No 0.3
5/6
Q5 [20 Marks]
We are given two data points with 2 different timestamps.
At the timestamp t = 1, we have a data point (x1, x2, y) where (x1, x2) = (0.3, 0.6) and y = 0.2.
At the timestamp t = 2, we have a data point (x1, x2, y) where (x1, x2) = (0.1, 1.0) and y = 0.4.
Here, x1 and x2 are 2 input variables. y is the output variable.
(a) Consider the traditional LSTM model. Initially, we have the following internal weight vectors and bias
variables as follows.