Body: <p>I have a hard time formulating this question(I'm not knowledgeable enough I think), so I'll give an example first and then the question:</p>
  
  <p>You have a table of data, let's say the occupancy of a building during the course of the day; each row has columns like "people_inside_currently", "apartment_id", "hour_of_day", "month", "year", "name_of_day"(monday-sunday), "amount_of_kids", "average_income" etc.</p>
  
  <p>You might preprocess two columns into a column "percent_occupied_during_whole_day" or something like that, and you want to group the data points in accordance with this as the main focus.</p>
  
  <p>What I'm wondering is: why use machine learning(particularly unsupervised clustering) for this? Why not just put it into an SQL database table(for example), calculate two columns into that new one, sort by descending order, and then split it into "top 25%, next 25%, next 25%, last 25%" and output this as "categories of data"? This is simpler, isn't it? I don't see the value of, for instance, making a Principle Component Analysis on it, reducing columns to some "unifying columns" which you don't know what to call anymore, and looking at the output of that, when you can get so much clearer results by just simply sorting and dividing the rows like this? I don't see the use of unsupervised clustering, I've googled a bunch of terms, but only found tutorials and definitions, applications(which seemed unnecessarily complex for such simple work), but no explanation of this.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-04T21:12:42.477Z
Comments.Text: Hi and welcome to this community! What do you mean by "calculate two columns into that new one" and "sort by descending order"? Anyway, machine learning algorithms usually find _approximative_ solutions, by searching for patterns in the data, when an exact solution may be infeasible.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-04T22:11:48.607Z
Comments.Text: Two columns to one = using PCA to reduce the dimensionality of the data so that it can be presented in a 2D graph. Sort by descending order = when you have the new column made by PCA, you can sort these with highest-to-lowest. Then you see which ones are "occupied most of the time" and which ones are less, and you can just divide that list into several groups or clusters(I guess) to see which ones are of what category/cluster instead of using clustering algorithms.
Comments.UserDisplayName: Jack Of Blades
Tags: <machine-learning>
Title: Why machine learning instead of simple sorting and grouping?
UserDisplayName: Jack Of Blades
ThreadText: # Why machine learning instead of simple sorting and grouping?
  
  (Asked by: username_0 on 2020-01-04)
  
  I have a hard time formulating this question(I'm not knowledgeable enough I think), so I'll give an example first and then the question:
  
  You have a table of data, let's say the occupancy of a building during the course of the day; each row has columns like "people\_inside\_currently", "apartment\_id", "hour\_of\_day", "month", "year", "name\_of\_day"(monday-sunday), "amount\_of\_kids", "average\_income" etc.
  
  You might preprocess two columns into a column "percent\_occupied\_during\_whole\_day" or something like that, and you want to group the data points in accordance with this as the main focus.
  
  What I'm wondering is: why use machine learning(particularly unsupervised clustering) for this? Why not just put it into an SQL database table(for example), calculate two columns into that new one, sort by descending order, and then split it into "top 25%, next 25%, next 25%, last 25%" and output this as "categories of data"? This is simpler, isn't it? I don't see the value of, for instance, making a Principle Component Analysis on it, reducing columns to some "unifying columns" which you don't know what to call anymore, and looking at the output of that, when you can get so much clearer results by just simply sorting and dividing the rows like this? I don't see the use of unsupervised clustering, I've googled a bunch of terms, but only found tutorials and definitions, applications(which seemed unnecessarily complex for such simple work), but no explanation of this.
  
  **Tags:** machine-learning
  > **Comments:**
  > **username_1**: Hi and welcome to this community! What do you mean by "calculate two columns into that new one" and "sort by descending order"? Anyway, machine learning algorithms usually find _approximative_ solutions, by searching for patterns in the data, when an exact solution may be infeasible.  
  > **username_0**: Two columns to one = using PCA to reduce the dimensionality of the data so that it can be presented in a 2D graph. Sort by descending order = when you have the new column made by PCA, you can sort these with highest-to-lowest. Then you see which ones are "occupied most of the time" and which ones are less, and you can just divide that list into several groups or clusters(I guess) to see which ones are of what category/cluster instead of using clustering algorithms.  
  
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  ## 0 answers
  
  