Clusterize item set with items as vectors of features I have to clusterize this dataset in which I have houses and water consumption in this form:
$$
House1 = (x_{1},x_{2}... x_{n});\\
House2 = (y_{1},y_{2}... y_{n});\\
House3 = (z_{1},z_{2}... z_{n});\\
$$
where $x_{i}$ is the daily consumption in liters while  $n$ is a fixed parameter (length of dataset).
I need to cluster these houses in k clusters based on their water consumption.
My question is: how can I handle data expressed in this form to feed in the clustering algorithm?
Maybe I will have to agglomerate each vector in some real value?
1.you just have to represent those  features as numeric in a vector
eg:[2,4,8,10]
2.Its a good  practise to normalize vector i just took sum of elements and divide by each element by that sum of elements  =[0.06666666666666667,0.13333333333333333,0.2,0.26666666666666666,0.3333333333333333]
normalize the values in that vector to be between 0 -1
3.feed the vectors into clustering algorithm (you can try with kmeans)