Talk:K-nearest neighbors algorithm

message: Comoplexly of kNN is O(n^2). I think so "It's guess"
Complexly of NNS(Nearest Neighbor Search) is O(nlogn) on the KB-tree, finding one item.
I'm studing a kNN in multiple nodes.
so I made Hk-NN is called by kNN.
Processing time reduced effectively.
I need to share and discussion in order to find out better Hk-NN method.
I don't know cost of kNN, and anyone who are known cost did nothing but guessing.
If you known kNN's processing cost with accuracy, please send a message by the mail.
My e-mail: hanjjon@gmail.com
:Maybe this draft version of an information retrieval book will help you. Chapter 14 discusses kNN. http://www-csli.stanford.edu/~hinrich/information-retrieval-book.html Hope this helps. --GrandiJoos 07:59, 1 October 2007 (UTC)
thread_title:  refly to other something..... 

message: How did any of this Kriging stuff get in here (history)?  —Preceding unsigned comment added by 66.92.232.105 (talk) 23:15, 16 March 2008 (UTC)
thread_title:  Kriging? 

message: I agree with previous comment. KNN is a statistical clustering algorith which uses density linkage. Maybe the two subjects should be treated separately? 196.36.217.137 (talk) 17:23, 3 December 2008 (UTC)
replies: text: This article is all about instance learning, which is perhaps the most common reason to find the k-nearest-neighbors, but there are many other uses as well. For example, the k-nearest-neighbors are often used to establishing neighborhood metrics for manifold learning. I propose that we make another article called "knn_instance_learner" and move all the stuff about regression and classification into it. This article should be about algorithms for finding the k-nearest neighbors (like kd-trees, Ball-trees, etc), no matter what the reason is for doing so. --128.187.80.2 (talk) 22:31, 9 December 2008 (UTC)
replies: text:  No, keep it here IMO. The other article you have in mind is Nearest neighbor search --mcld (talk) 20:42, 20 October 2009 (UTC)
thread_title: Split this topic in two?

message: I suggest paying attention which spelling the authors use. There is now both British and American spelling used in the article.
Wassermann7 (talk) 21:48, 19 July 2010 (UTC)
thread_title:  neighbor vs neighbour 

message: For me this looks more like a Parzen window. I think to make the representation clear, the circles have go exact though the center of the kth sample. Maybe the black of the circle boundary can be even a bit reduced towards gray. Also some links from the test sample to the training samples could be done, together with some counting numbers, to make clear that the radius of the neighbourhood depends only upon the distance to the closest training samples. 193.52.240.233 (talk) 02:29, 10 January 2011 (UTC)
thread_title:  Schematic image is not clear 

message: I removed the line about the optimal k being 10.  Following the paper trail, the best that can be said is that when analyzing some kinds of forestry data, a k between 10 and 15 has been found to work well, nothing more.
Helpre (talk) 17:37, 1 April 2011 (UTC)
thread_title:  Optimal k = 10 

message: The good faith move to k-nearest neighbors algorithm has been reverted, because both scholarly and non-scholarly searches show that the singular name for the algorithm is a bit more well-used in the vernacular.  Remember, the issue here is the name of the algorithm, not how many neighbors are computed.  If anyone is in disagreement with this revert, then please don't hesitate to voice your opinion.  It is, of course, okay with me if anyone would like to open a move request discussion. – Paine Ellsworth CLIMAX! 20:03, 7 September 2013 (UTC)
thread_title:  Plural neighbor 

message: under the section "For regression" it says:
"Find a heuristically optimal number k of nearest neighbors, based on RMSE. This is done using cross validation."
Is there some reference/further details to this hinted at method? I am searching now without much luck, seems to me like it should be referenced.
37.11.59.68 (talk) 20:55, 13 December 2013 (UTC)
thread_title:  For regression 

message: Maybe k-NN is somehow related to non-parametric statistics, but for sure it's not a non-parametric method. The first sentence is at least misleading, and at best hilarious since the algorithm has its parameter explicitly in its name... :)
BartoszKP (talk) 14:40, 1 April 2014 (UTC)
replies: text: Exactly what I wanted to write in this discussion. --178.12.145.158 (talk) 11:53, 1 April 2017 (UTC)
replies: text: The fact that thees two comments were made at the same day three years apart, and in particular at this specific day, is somewhat curious but otherwise irrelevant. --178.12.145.158 (talk) 11:54, 1 April 2017 (UTC)

text: I'm confused about method and model here. Nonparametric statistics lists kNN as a model. Would that be more appropriate?  — Preceding unsigned comment added by Rakeroot (talk • contribs) 19:11, 19 February 2019 (UTC)
thread_title:  Non-parametric? Really? 

message: Yes, there are weighted kNN algorithms, and some can be VERY good.
The one used in the CRM114 KNN classifier uses a 1/r^2 weighting, so all known samples contribute a little, but the contribution of far-away examples is extremely small.
I would put an entry in, but since I'm the author of CRM114, it would be a little too close to being a primary source.  So - if someone else would care to read the following paper from the US NIST repository (that being somewhat reputable):
  
  http://trec.nist.gov/pubs/trec15/papers/crm.spam.final.pdf
especially section titled "Hypothesis 4" and then write it up here.
Thanks!  (I hope this is the right way to handle "no primary publication- give good citations" principles)   Dr. Crash (talk) 20:08, 15 June 2016 (UTC)
replies: text: The recent merge in from Nearest neighbour classifiers  brings in a section on weighted kNN algorithms. Klbrain (talk) 13:13, 1 September 2016 (UTC)
thread_title:  Re: "Are there not any weighted kNN algorithms?" 

message: Is there some way that we could make the formulas in "Statistical Setting" a little bit more inviting? Perhaps writing it again in simple English or linking to some of the concepts? Not having a background in statistics, I have no idea what "fancy R raised to the d" means nor how to search for it. The rest is similarly meaningless to me. I understand that the format used is concise and precise for it's target audience but it's a huge roadblock for people outside that audience.  — Preceding unsigned comment added by TomCerul (talk • contribs) 17:54, 28 April 2022 (UTC)
thread_title:  Make formulas more useful to newbies? 

message: There's a mention in the text of the condition that "k_n := k diverges." I don't know what this means and I'm not familiar with the := notation. And I'm a pretty advanced reader! Can someone clarify this?  — Preceding unsigned comment added by 2603:8080:2807:EDC:9DD9:872F:900D:7D64 (talk) 09:22, 4 June 2022 (UTC)
thread_title:  k_n := k diverges? 

message: There is an unhelpful, possibly old, link to another Wikipedia page. The outbound link is in the section called, "The weighted nearest neighbour classifier." The phrase "bagged nearest neighbour classifier" is linked to a page called, "Bootstrap aggregating". Not only is the linked page title different from the linked text, the linked page only mentions K nearest neighbors once and in an off-hand way. The linked page does not shed light on the topic of this page. In fact, it seems to contradict the statement made in this page. I suggest deleting the sentence that contains the link.
Jason.Rafe.Miller (talk) 12:15, 23 June 2022 (UTC)
thread_title:   bagged nearest neighbour classifier