Timeline for Quick search the most similar objects in the n-dimensional space
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 28, 2016 at 20:43 | comment | added | Rick James | 50%, etc -- Aren't you are still stuck with touching every row in the dataset? I assume this is the main performance issue. (Computations are a lot cheaper than fetching rows.) | |
| Dec 28, 2016 at 19:31 | comment | added | konstantin_doncov | [2 part] Also please check this question which describe current question better. | |
| Dec 28, 2016 at 19:30 | comment | added | konstantin_doncov | [1 part] I still can't find the answer. Unfortunately, KD-tree is not a good solution for a data with a many dimensions(I will have more than 120 dimensions), maybe I can use something like your solution but improve it using preliminary sorting? Every record has dimension with the biggest diference from the mean coordinate of this dimension(I have described it better in the question). So we can filter 50% the most closest records by the coord with the biggest difference, then filter 50% from previous query by the coord with the the second largest difference and so on. | |
| Dec 21, 2016 at 7:11 | comment | added | Rick James | Or maybe an R-Tree? | |
| Dec 21, 2016 at 5:41 | comment | added | konstantin_doncov | Thanks for the answer! About plan A: if we will have big dataset I think search will take a lot of time. So it's main big problem, maybe a KD-tree can be a good solution? | |
| Dec 20, 2016 at 23:03 | history | answered | Rick James | CC BY-SA 3.0 |