What is an appropriate machine learning technique to analyse development of status over time? I have a dataset as follows (not the actual data, but representative):

User   Age  Salary  Status_2000  Status_2001  Status_2002  Status_2003  ... Status_2019
John   30   10000   A            A            A            B            ...  A
Mary   25   20000   A            B            B            B            ...  C
Jacob  35   30000   E            F            F            F            ...  A
Jenny  28   22000   A            F            F            A            ...  F


I have over 50,000 rows and I want to perform an analyse the development patterns of the Status from 2000 to 2019. However, the Status are encoded with letters from A to J. I would like to ask whether there are any machine learning methods or clustering methods which could help in this analysis? I initially thought of K-Means but I'm not sure if that is appropriate even with Label Encoding. 

In the same dataset, there are also the following columns:

User   Age  Salary  2000_%  2001_%  2002_%  2003_%  ... 2019_%
John   30   10000   0%      0%      20%     25%     ... 100%
Mary   25   20000   0%      10%     40%     60%     ... 80%
Jacob  35   30000   0%      80%     100%    80%     ... 80%
Jenny  28   22000   0%      0%      0%      20%     ... 60%


where the data represents % of the maximum amount from 2000 to 2019, and I want to perform an analysis on the development of the %. Are there any techniques suitable for such analysis?

Thank you all for your help in advance!
Yes, K-means is an option. But you need to do some data processing first.


My first concern is about quantifying those statuses (are they linearly depended, like does B means something in the middle of A and C)? then numerate them (A to 1 and B to 2 and C to 3 etc)
Represent your data set in a dimensional way, melt years columns into three columns 


Year (with values of 2000, 2001, 2003 etc) so you get a time series dimension.
Year_status (status value).
Year_percentage (percentage of the year).

Try to treat pattern by moving window (kernel), and try different sizes.. for example size of 3 will cluster every 3 followed statuses into patterns centroids (for example.. having a common occurrence of A, E, A)