Find minimum edit distance between two words, minimum edit distance solved exercise, how to use minimum edit distance to find the distance between two strings? how to use dynamic programming for finding edit distance?
Find minimum edit distance between two strings
Question:
Find
the minimum edit distance in transforming the word DOG to COW using insertion, deletion, and substitution cost as 1.
Solution:
Minimum edit distance is the minimum number of editing operations (insertion, deletion,
substitution) required to convert one string into another. The solution given here uses Basic Minimum Edit Distance which uses cost for all operations as 1.
Each
cell in the distance matrix contains the distance between two strings. For
instance, the cell intersect at i, j (distance[i, j]) contains the distance
between first i characters of the target and the first j characters of the
source. The value for each cell is calculated as per the equation shown below;
Step 1: Draw the edit
distance matrix. In this, each word is preceded by # symbol which marks the
empty string.



Step 2: Find the editdistance
values using minimum edit distance algorithm to convert # (row side) to #COW (column side) and populate appropriate cells
with the calculated distance.
Number of
operations required to convert;



Step 3: Find the editdistance
values using minimum edit distance algorithm to convert # (column side) to #DOG (row side) and populate appropriate
cells with the calculated distance.
Number of operations
required to convert



So far, we have
found the minimum edit distance for 7 subproblems.
[#  # = 0, #  #C
= 1, #  #CO = 2, #  #COW = 3, #  #D = 1, #  #DO = 2, and #  #DOG = 3]


Step 4: From this step
onward, we try to find the cost for a subproblem by finding the minimum cost
of three subproblems and add 1 with that if the characters intersect at that
cell are different. If the intersecting characters are same, then we add 0
with the diagonal cell value.
[Note: we have used A as the name for this matrix and
included the index numbers for easy understanding]
From this step onward, use the following table as a key to
find the cost.
If the row character ≠ column character,
If the row character = column character,


Edit distance for Row 1


Subproblem: #D ➔ #C.
Intersecting
cell: A[1,
1]
Same intersecting
characters? NO.
Cost(A[1, 1]) = Min(A[0, 0], A[0, 1], A[1, 0]) + 1
= A[0, 0] + 1
=
0 + 1 = 1



Subproblem: #D ➔ #CO.
Intersecting
cell: A[1,
2]
Same intersecting
characters? NO.
Cost(A[1, 2]) = Min(A[0, 1], A[0, 2], A[1, 1]) + 1
= A[1, 1] + 1
= 1 + 1 = 2



Subproblem: #D ➔ #COW.
Intersecting
cell: A[1,
3]
Same intersecting
characters? NO.
Cost(A[1, 3]) = Min(A[0, 2], A[0, 3], A[1, 2]) + 1
= A[1, 2] + 1
= 2 + 1 = 3



Edit distance for Row 2


Subproblem: #DO ➔ #C.
Intersecting
cell: A[2,
1]
Same intersecting
characters? NO.
Cost(A[2, 1]) = Min(A[1, 0], A[1, 1], A[2, 0]) + 1
= A[1, 0] + 1
= 1 + 1 = 2



Subproblem: #DO ➔ #CO.
Intersecting
cell: A[2, 2]
Same
intersecting characters? YES
Cost(A[2, 2]) = Min(A[1, 1], A[1, 2], A[2, 1]) + 0
= A[1, 1] + 0
= 1 + 0 = 1



Subproblem: #DO ➔ #COW.
Intersecting
cell: A[2,
3]
Same intersecting
characters? NO.
Cost(A[2, 3]) = Min(A[1, 2], A[1, 3], A[2, 2]) + 1
= A[2, 2] + 1
= 1 + 1 = 2



Edit distance for Row 3


Subproblem: #DOG ➔ #C.
Intersecting
cell: A[3,
1]
Same intersecting
characters? NO.
Cost(A[3, 1]) = Min(A[2, 0], A[2, 1], A[3, 0]) + 1
= A[2, 0] + 1
= 2 + 1 = 3



Subproblem: #DOG ➔ #CO.
Intersecting
cell: A[3,
2]
Same intersecting
characters? NO
Cost(A[3, 2]) = Min(A[2, 1], A[2, 2], A[3, 1]) + 1
= A[2, 2] + 1
= 1 + 1 = 2



Subproblem: #DOG ➔ #COW.
Intersecting
cell: A[3,
3]
Same intersecting
characters? NO.
Cost(A[3, 3]) = Min(A[2, 2], A[2, 3], A[3, 2]) + 1
= A[2, 2] + 1
= 1 + 1 = 2


The last cell (A[3, 3]) holds the minimum edit distance between the given strings DOG and COW.
Alignment:
The alignment between DOG and COW is as follows;
Alignment:
The alignment between DOG and COW is as follows;
D
O
G

 
C
O
W
Substitute No Substitute
Cost
= 1 change Cost = 1
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