DTSA 5001 EXAMINATION STUDY SHEET
2026 VERIFIED QUESTION–ANSWER
COLLECTION
◉ Symmetric Variables Equation
Answer: ((r + s) / (q + r + s + t)) = d(i,j)
◉ Asymmetric Variables Equation
Answer: (q / (q+r+s) or 1 - d(i,j)) = sim(i,j) or Jaccard coefficient ;
d(i,j) = (r + s)/(q + r + s)
◉ Jaccard coefficient
Answer: (q / (q+r+s))
◉ Ordinal Attributes
Answer: for all r(if) in {1,...,Mf}, z(if) = (r(if) - 1)/(Mf - 1)
◉ Numeric Object Dissimilarity
Answer: Usually measured by distance with Minkowski distance (I_p
norm)
, ◉ Minkowski Distance
Answer: d(i,j) = (abs(xi1, xj1)**p + ... + abs(xin, xjn)***p)**(1/p),
where p=1 (Manhattan Distance) or p=2 (Euclidean Distance)
◉ Distance Measure Properties
Answer: d(i,j) <= d(i,k) + d(k,j), triangular inequality
◉ cosine similarity
Answer: cos(A,B) = (A*B) / ||A||||B|| = (A*B) / (sum(A)^2 *
sum(B)^2)
◉ What operations are involved with sequential data and time
series?
Answer: Euclidean matching, dynamic time warping, minimum jump
cost
◉ Mixed Attribute Types
Answer: Weighted sum across attributes. d(i,j)=(sum(dij,
dij))/sum(dij)
◉ When to use Euclidean/Manhattan processes?
Answer: Dense, continuous data
2026 VERIFIED QUESTION–ANSWER
COLLECTION
◉ Symmetric Variables Equation
Answer: ((r + s) / (q + r + s + t)) = d(i,j)
◉ Asymmetric Variables Equation
Answer: (q / (q+r+s) or 1 - d(i,j)) = sim(i,j) or Jaccard coefficient ;
d(i,j) = (r + s)/(q + r + s)
◉ Jaccard coefficient
Answer: (q / (q+r+s))
◉ Ordinal Attributes
Answer: for all r(if) in {1,...,Mf}, z(if) = (r(if) - 1)/(Mf - 1)
◉ Numeric Object Dissimilarity
Answer: Usually measured by distance with Minkowski distance (I_p
norm)
, ◉ Minkowski Distance
Answer: d(i,j) = (abs(xi1, xj1)**p + ... + abs(xin, xjn)***p)**(1/p),
where p=1 (Manhattan Distance) or p=2 (Euclidean Distance)
◉ Distance Measure Properties
Answer: d(i,j) <= d(i,k) + d(k,j), triangular inequality
◉ cosine similarity
Answer: cos(A,B) = (A*B) / ||A||||B|| = (A*B) / (sum(A)^2 *
sum(B)^2)
◉ What operations are involved with sequential data and time
series?
Answer: Euclidean matching, dynamic time warping, minimum jump
cost
◉ Mixed Attribute Types
Answer: Weighted sum across attributes. d(i,j)=(sum(dij,
dij))/sum(dij)
◉ When to use Euclidean/Manhattan processes?
Answer: Dense, continuous data
