Timeline for Clustering in real life world problems
Current License: CC BY-SA 4.0
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| Apr 16, 2019 at 18:26 | comment | added | Has QUIT--Anony-Mousse | You can use whatever you want. And you can interpret what you can interpret. That sounds banal, but the point is that this is a choice that you make, essentially how to weight one bed squared vs. one pool squared vs. one dollar squared. There is no right. But you may or may not be able to argue that the result is meaningful for your use case. | |
| Apr 16, 2019 at 14:00 | comment | added | No_Body | Thanks for the replies guys. I highly appreciate it. @Anony-Mousse, apologies, but when you say very different meanings. Does this mean they should have same measurements. For example, If i have similar features like (no. of bed, no. of bath, no. of garage) OR (area of bed, area of bath, area of garage) then I can cluster them ? The data-set given in question has mix of all kinds of features so i can't use K-Means here?Sorry I am still kind of new to this area. | |
| Apr 16, 2019 at 10:00 | comment | added | Nick Cox | Even normalization (whatever that means precisely) isn't guaranteed to make k-means defensible. | |
| Apr 16, 2019 at 9:59 | history | edited | Nick Cox | CC BY-SA 4.0 |
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| Apr 16, 2019 at 9:36 | comment | added | Has QUIT--Anony-Mousse | Nevertheless your features have very different meanings. So what is the meaning of the function you optimized? | |
| Apr 16, 2019 at 9:33 | history | edited | Has QUIT--Anony-Mousse | CC BY-SA 4.0 |
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| Apr 12, 2019 at 14:34 | comment | added | No_Body | Hi, You're right about the zipcodes. I only used Price, area, no of bedroom, bathrooms after normalization for clustering. I computed 4 clusters for every Zip code.I didn't include zip codes in the data itself. | |
| Apr 12, 2019 at 6:12 | history | answered | Has QUIT--Anony-Mousse | CC BY-SA 4.0 |