Is the DBSCAN pseudocode shown on wikipedia page flawed?
$begingroup$
Referenced from this paper, the follow pseudocode is shown on the DBSCAN wikipedia page:
DBSCAN(DB, distFunc, eps, minPts) {
C = 0 /* Cluster counter */
for each point P in database DB {
if label(P) ≠ undefined then continue /* Previously processed in inner loop */
Neighbors N = RangeQuery(DB, distFunc, P, eps) /* Find neighbors */
if |N| < minPts then { /* Density check */
label(P) = Noise /* Label as Noise */
continue
}
C = C + 1 /* next cluster label */
label(P) = C /* Label initial point */
Seed set S = N {P} /* Neighbors to expand */
for each point Q in S { /* Process every seed point */
if label(Q) = Noise then label(Q) = C /* Change Noise to border point */
if label(Q) ≠ undefined then continue /* Previously processed */
label(Q) = C /* Label neighbor */
Neighbors N = RangeQuery(DB, distFunc, Q, eps) /* Find neighbors */
if |N| ≥ minPts then { /* Density check */
S = S ∪ N /* Add new neighbors to seed set */
}
}
}
}
The problem I found with this code is that it does not visit points which has been processed (marked as noise or part of a cluster); this is unsafe. For example, if I have four points as shown in the picture below, where the points are indicated by blue circles and the number above each point is its sequence in the database DB.
Given distFunc = EuclideanDist; eps = 1.01; minPts = 2;, with the above code, I think I will get two clusters: {Point_1, Point_3} and {Point_2, Point_4}. However, isn't the correct answer be a single cluster formed with all four points?
Am I correct or wrong?
clustering
asked 3 mins ago
AnthonyAnthony
1011
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add a comment |
$begingroup$
Referenced from this paper, the follow pseudocode is shown on the DBSCAN wikipedia page:
DBSCAN(DB, distFunc, eps, minPts) {
C = 0 /* Cluster counter */
for each point P in database DB {
if label(P) ≠ undefined then continue /* Previously processed in inner loop */
Neighbors N = RangeQuery(DB, distFunc, P, eps) /* Find neighbors */
if |N| < minPts then { /* Density check */
label(P) = Noise /* Label as Noise */
continue
}
C = C + 1 /* next cluster label */
label(P) = C /* Label initial point */
Seed set S = N {P} /* Neighbors to expand */
for each point Q in S { /* Process every seed point */
if label(Q) = Noise then label(Q) = C /* Change Noise to border point */
if label(Q) ≠ undefined then continue /* Previously processed */
label(Q) = C /* Label neighbor */
Neighbors N = RangeQuery(DB, distFunc, Q, eps) /* Find neighbors */
if |N| ≥ minPts then { /* Density check */
S = S ∪ N /* Add new neighbors to seed set */
}
}
}
}
The problem I found with this code is that it does not visit points which has been processed (marked as noise or part of a cluster); this is unsafe. For example, if I have four points as shown in the picture below, where the points are indicated by blue circles and the number above each point is its sequence in the database DB.
Given distFunc = EuclideanDist; eps = 1.01; minPts = 2;, with the above code, I think I will get two clusters: {Point_1, Point_3} and {Point_2, Point_4}. However, isn't the correct answer be a single cluster formed with all four points?
Am I correct or wrong?
clustering
asked 3 mins ago
AnthonyAnthony
1011
New contributor
Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Referenced from this paper, the follow pseudocode is shown on the DBSCAN wikipedia page:
DBSCAN(DB, distFunc, eps, minPts) {
C = 0 /* Cluster counter */
for each point P in database DB {
if label(P) ≠ undefined then continue /* Previously processed in inner loop */
Neighbors N = RangeQuery(DB, distFunc, P, eps) /* Find neighbors */
if |N| < minPts then { /* Density check */
label(P) = Noise /* Label as Noise */
continue
}
C = C + 1 /* next cluster label */
label(P) = C /* Label initial point */
Seed set S = N {P} /* Neighbors to expand */
for each point Q in S { /* Process every seed point */
if label(Q) = Noise then label(Q) = C /* Change Noise to border point */
if label(Q) ≠ undefined then continue /* Previously processed */
label(Q) = C /* Label neighbor */
Neighbors N = RangeQuery(DB, distFunc, Q, eps) /* Find neighbors */
if |N| ≥ minPts then { /* Density check */
S = S ∪ N /* Add new neighbors to seed set */
}
}
}
}
The problem I found with this code is that it does not visit points which has been processed (marked as noise or part of a cluster); this is unsafe. For example, if I have four points as shown in the picture below, where the points are indicated by blue circles and the number above each point is its sequence in the database DB.
Given distFunc = EuclideanDist; eps = 1.01; minPts = 2;, with the above code, I think I will get two clusters: {Point_1, Point_3} and {Point_2, Point_4}. However, isn't the correct answer be a single cluster formed with all four points?
Am I correct or wrong?
clustering
asked 3 mins ago
AnthonyAnthony
1011
New contributor
Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Referenced from this paper, the follow pseudocode is shown on the DBSCAN wikipedia page:
DBSCAN(DB, distFunc, eps, minPts) {
C = 0 /* Cluster counter */
for each point P in database DB {
if label(P) ≠ undefined then continue /* Previously processed in inner loop */
Neighbors N = RangeQuery(DB, distFunc, P, eps) /* Find neighbors */
if |N| < minPts then { /* Density check */
label(P) = Noise /* Label as Noise */
continue
}
C = C + 1 /* next cluster label */
label(P) = C /* Label initial point */
Seed set S = N {P} /* Neighbors to expand */
for each point Q in S { /* Process every seed point */
if label(Q) = Noise then label(Q) = C /* Change Noise to border point */
if label(Q) ≠ undefined then continue /* Previously processed */
label(Q) = C /* Label neighbor */
Neighbors N = RangeQuery(DB, distFunc, Q, eps) /* Find neighbors */
if |N| ≥ minPts then { /* Density check */
S = S ∪ N /* Add new neighbors to seed set */
}
}
}
}
The problem I found with this code is that it does not visit points which has been processed (marked as noise or part of a cluster); this is unsafe. For example, if I have four points as shown in the picture below, where the points are indicated by blue circles and the number above each point is its sequence in the database DB.
Given distFunc = EuclideanDist; eps = 1.01; minPts = 2;, with the above code, I think I will get two clusters: {Point_1, Point_3} and {Point_2, Point_4}. However, isn't the correct answer be a single cluster formed with all four points?
Am I correct or wrong?
clustering
clustering
asked 3 mins ago
AnthonyAnthony
1011
New contributor
Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
AnthonyAnthony
1011
New contributor
Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
AnthonyAnthony
1011
New contributor
Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
AnthonyAnthony
1011
asked 3 mins ago
AnthonyAnthony
1011
1011
New contributor
Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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