# Prepping Data For Usage Clustering

Dataset: I'm given the number of minutes individual customers use a product each day and am trying to cluster this data in order to find common usage patterns.

My question: How can I format the data so that, for example, a power user with high levels of use for a year looks the same as a different power user who has only been able to use the device for a month before I ended data collection?

So far I've turned each customer into an array where each cell is the number of minutes used that day. This array starts when the user first uses the product and ends after the user's first year of use. All entries in the cells must be double values (e.x. 200.0 minutes used) for the clustering model. I've considered either setting all cells/days after the last day of data collection to either -1.0 or NULL. Are either of these a valid approach? If not what would you suggest?

Therefore you should look into clustering techniques and distance metrics which allow for these properties. I don't know your language of choice but here are some of the many packages in R that you might find interesting :
- Dynamic Time Warping included in base R
This would also solve your data formatting problem as to setting values to -1 or NULL would not be needed anymore. hth.