Row 92623
Content Data
This page contains data entry 92623 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
Depends a lot on what you want to do with the clusters. I generally go elbow if I get a clear answer. If not, silhouette can help narrow a range for K. I can check the porportion of inertia explained by the clusters and set an arbitrary threshold. Sometimes I check at the coordinates of the centers and stop when K+1 creates centers that are "too close to one another" (kind of a similar idea to silhouette, but I'm not looking at the average over all dataset but at the proximity of the two closest centers). Or when I have no interpretation of the extra clusters, or...
Basically it really depends on what I want to do with those. Different use cases may require different levels of rigor.
To my knowledge, there is no objectively best way to select K.
| Field | Value |
|---|---|
| text | Depends a lot on what you want to do with the clusters. I generally go elbow if I get a clear answer. If not, silhouette can help narrow a range for K. I can check the porportion of inertia explained by the clusters and set an arbitrary threshold. Sometimes I check at the coordinates of the centers and stop when K+1 creates centers that are "too close to one another" (kind of a similar idea to silhouette, but I'm not looking at the average over all dataset but at the proximity of the two clo… |
| label | r/machinelearning |
| dataType | comment |
| communityName | r/MachineLearning |
| datetime | 2024-05-25 |
| username_encoded | Z0FBQUFBQm5Lak1zVG12dy1scExXLXZGUEJJd3ZfdDNJbVZDWjVtUzg2Vy1aN2U5c2ZDbEhEODFDQ3ZhTW1KaUpUVXlYNThPRTJLTG13dHhlSDdrMGZtZ05CTlFtZFlJV3c9PQ== |
| url_encoded | Z0FBQUFBQm5Lak8tLS1iYzhRRXY3VDZFd3EySENzVE9mU0Z0NDBfaEVlRDA1Q2dNa0Y4cWdHQUpaQVgtUmNldjk2TkhfRUE2S0wzRXpaaURjZ1BfN3ZseHlnak84cXVwQnozRmRWQXFvREw3MFhjamNVSFQ0cl9oamF0Sm0wLWlNR3FGSjBzbndqN3ZpcjV3M1RFazFDSXZucG8wNmtDQXphRnMtNVpLbTlOODN4bDFyZDd2bkx2MGluWTRqejNJSFQwdHpIb3ZZbjlHWnpGaEpJRG5qN3Y5OXRCdFZiemVMdz09 |
Raw Record
{
"text": "Depends a lot on what you want to do with the clusters. \nI generally go elbow if I get a clear answer. If not, silhouette can help narrow a range for K. \nI can check the porportion of inertia explained by the clusters and set an arbitrary threshold. Sometimes I check at the coordinates of the centers and stop when K+1 creates centers that are \"too close to one another\" (kind of a similar idea to silhouette, but I'm not looking at the average over all dataset but at the proximity of the two closest centers). Or when I have no interpretation of the extra clusters, or... \n\nBasically it really depends on what I want to do with those. Different use cases may require different levels of rigor. \n\nTo my knowledge, there is no objectively best way to select K.",
"label": "r/machinelearning",
"dataType": "comment",
"communityName": "r/MachineLearning",
"datetime": "2024-05-25",
"username_encoded": "Z0FBQUFBQm5Lak1zVG12dy1scExXLXZGUEJJd3ZfdDNJbVZDWjVtUzg2Vy1aN2U5c2ZDbEhEODFDQ3ZhTW1KaUpUVXlYNThPRTJLTG13dHhlSDdrMGZtZ05CTlFtZFlJV3c9PQ==",
"url_encoded": "Z0FBQUFBQm5Lak8tLS1iYzhRRXY3VDZFd3EySENzVE9mU0Z0NDBfaEVlRDA1Q2dNa0Y4cWdHQUpaQVgtUmNldjk2TkhfRUE2S0wzRXpaaURjZ1BfN3ZseHlnak84cXVwQnozRmRWQXFvREw3MFhjamNVSFQ0cl9oamF0Sm0wLWlNR3FGSjBzbndqN3ZpcjV3M1RFazFDSXZucG8wNmtDQXphRnMtNVpLbTlOODN4bDFyZDd2bkx2MGluWTRqejNJSFQwdHpIb3ZZbjlHWnpGaEpJRG5qN3Y5OXRCdFZiemVMdz09"
}
Entry Information
- Entry ID: 92623
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000