title: How to Deal With Different Scales of Values for Each Coordinate in K-Means Algorithm?
body: <p>I am practicing K-means using python. 
  I am working with a set of shopping data, and the input dataset looks something like this.</p>
  
  <p>Input table headers:</p>
  
  <ul>
  <li>[userID]</li>
  <li>[money_spent_on_clothes] int </li>
  <li>[money_spent_on_food] int</li>
  <li>[money_spent_on_shoes] int</li>
  <li>[money_spent_on_Monday] int</li>
  <li>[money_spent_on_Tuesday] int</li>
  <li>[money_spent_on_Wednesday] int</li>
  <li>[money_spent_on_Thursday] int</li>
  <li>[money_spent_on_Friday] int</li>
  <li>[shopping_hours_am] int</li>
  <li>[shopping_hours_pm] int </li>
  </ul>
  
  <p>I want to use how much they spend in each category, what day they shop the most, and how many hours they spend in shopping during the day into consideration when clustering users. </p>
  
  <p>There are few discrepancies in the dataset.</p>
  
  <ul>
  <li>units don't match: $ vs. hour</li>
  <li>feature types are mixed. First 3 features are about category, next 5 features are about day of the week, and the last 2 features are how much time they spend during the day (am vs. pm). </li>
  </ul>
  
  <p>I am wondering if K-means clustering can be done on this dataset &amp; features I have stated, and if it can be done, what kind of normalisation must be applied in order for this to work? </p>
  
  <p>Thanks in advance!</p>