Forecasting data that has both additive and multiplicative seasonality I wish to decompose the trend of the following time series.

The data is as follows,
index = c(96,1492,121,245,95,1621,144,366,158,1679,210,469,194,1796,265,589,305,
          1861,348,754,403,1853,490,979,465,1984,630)

It looks like it has both an additive, and a multiplicative component. How do I treat something like this?
 A: I took your 27 values and obtained the following model  and here  and here  . The residuals suggest sufficiency  and here  . The Actual/Fit and Forecast is here  . The Cleansed and the Actual  with forecast plot here 
In summary it is an additive Holt-Winters seasonal model with 3 anomalies ... This is the way I would "decompose" the original series to trend.. seasonal dummies ..and 3 pulses  ..leading to an error process free of structure.
In terms of your desire to "decompose the trend" , simply modify/cleanse the three errant/unusual observations using the 3 pulse coefficients and then use the coefficients 256.07 and 20.6019 to adjust the Y values for mean and trend  .
Some people (not to mention any names) are often concerned about fitting 8 parameters to 27 values ...so I reran and restricted the solution to not detect any anomalies/pulses. The good news is that only 5 parameters are estimated . The bad news is that the results are unacceptable . and a residual plot here  . Sometimes you have to listen to the data and validate the assumptions under which models are estimated.
