Talk:Nearest-neighbor chain algorithm

Nearest-neighbor chain algorithm has been listed as one of the Engineering and technology good articles under the good article criteria. If you can improve it further, please do so. If it no longer meets these criteria, you can reassess it. Review: March 7, 2017. (Reviewed version).

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This review is transcluded from Talk:Nearest-neighbor chain algorithm/GA3. The edit link for this section can be used to add comments to the review.

Reviewer: Shearonink (talk · contribs) 19:48, 1 March 2017 (UTC)[reply]

I wil be giving this article a Review for possible GA status. As higher mathematics are not one of my strong suits (my last "high math" was trigonometry & analytic geometry ages ago...) this might take me a while but I promise I will finish. Shearonink (talk) 19:48, 1 March 2017 (UTC)[reply]

GA review – see WP:WIAGA for criteria

1. Is it well written?

   A. The prose is clear and concise, and the spelling and grammar are correct:

   B. It complies with the manual of style guidelines for lead sections, layout, words to watch, fiction, and list incorporation:

2. Is it verifiable with no original research?

   A. It contains a list of all references (sources of information), presented in accordance with the layout style guideline:

   B. All in-line citations are from reliable sources, including those for direct quotations, statistics, published opinion, counter-intuitive or controversial statements that are challenged or likely to be challenged, and contentious material relating to living persons—science-based articles should follow the scientific citation guidelines:

   Still checking these out - trying to be thorough. Shearonink (talk) 05:48, 5 March 2017 (UTC)[reply]

   Good to go. Shearonink (talk) 16:33, 5 March 2017 (UTC)[reply]

   C. It contains no original research:

   No problems. Shearonink (talk) 16:33, 5 March 2017 (UTC)[reply]

   D. It contains no copyright violations nor plagiarism:

   Passed the copyvio tool with flying colors. Shearonink (talk) 04:22, 3 March 2017 (UTC)[reply]

3. Is it broad in its coverage?

   A. It addresses the main aspects of the topic:

   B. It stays focused on the topic without going into unnecessary detail (see summary style):

   I think so, but am reading through a few more times to make sure. Shearonink (talk) 16:33, 5 March 2017 (UTC)[reply]

   I think the article stays as focused as is possible and still make the subject as clear as an article in a non-technical encyclopedia can. Shearonink (talk) 07:08, 7 March 2017 (UTC)[reply]

4. Is it neutral?

   It represents viewpoints fairly and without editorial bias, giving due weight to each:

5. Is it stable?

   It does not change significantly from day to day because of an ongoing edit war or content dispute:

   No edit-wars. Shearonink (talk) 04:22, 3 March 2017 (UTC)[reply]

6. Is it illustrated, if possible, by images?

   A. Images are tagged with their copyright status, and valid fair use rationales are provided for non-free content:

   Looks good. Shearonink (talk) 04:22, 3 March 2017 (UTC)[reply]

   B. Images are relevant to the topic, and have suitable captions:

7. Overall:

   Pass or Fail:

   Nicely-done. As I said below, I think, for future improvements, that explaining the usage of Nearest-neighbor chain algorithms in real-world terms (as in, what do they do?) will help de-mystify the subject to Wikipedia's general readership. Shearonink (talk) 07:08, 7 March 2017 (UTC)[reply]

I am reading this through over and over and sort of/maybe/almost understand the subject. I do have a question though...in layman's terms, is there an explanation for what this algorithm is used for? I mean I understand it is used for clustering but what is the purpose of "clustering"? Shearonink (talk) 04:22, 3 March 2017 (UTC)[reply]
@David Eppstein: Was wondering about the above question. Thanks, Shearonink (talk) 05:48, 5 March 2017 (UTC)[reply]

Yes, thanks for the suggestion. The short answer is that clustering is fundamental for understanding all kinds of data — e.g. trying to understand which different diseases cause similar collections of symptoms, trying to group customers by their interests, etc. Hierarchical clustering is good either when the grouping of data that you want to construct is multi-level or tree-like (like Wikipedia categories) or when you don't know how many groups to make (so you make groupings at all levels of refinement and then figure out which level is the right one later). A common use for some of the clustering algorithms described here is to reconstruct evolutionary trees by using genetic distance. But all this should really be in the article (in the background section), not here — I plan on adding it when I can take the time to look for appropriate sources to use for it. —David Eppstein (talk) 07:58, 5 March 2017 (UTC)[reply]

Just trying to understand the subject a bit more, so thanks. And you are looking to add this type of content in the future? Ok, good, that was probably going to be a "recommendation for future improvements" from me. The article really looks to be in good shape, I will be doing a few more readthroughs to see if there's anything I missed, but should be able to finish up in the next day or so. Shearonink (talk) 16:33, 5 March 2017 (UTC)[reply]