What constitutes a unique research paper?
The following is general guidelines under the assumption that you're looking at this as a computer science project. If you specify the field, I can modify this advice accordingly. For advice about a particular project, you'll have to share the details of your project with someone with experience publishing in CS or whatever field you're working in.

The bar is, roughly, "is this something people would want to read." When you try to present new work to an audience, your job is pretty much to sell the arbiter (be it dissertation adviser, journal referee, or conference organizer) that the work that you did is something people would be interested in reading and using.

The most important question is: have other people used this algorithm, on any data set, before? If the answer is "no" than your work is almost certainly publishable, especially as it sounds like your algorithm preforms comparably well to other algorithms that preform the same or similar task. Your paper should explicitly analyze this and seek to give examples of data sets or contexts in which your algorithm out-preforms known algorithms.

If the answer is "yes," then your work may or may not be publishable. Applying a well-known algorithm like the Gale-Shipley Algorithm or Diijkstra's Algorithm to some random data set isn't generally going to be considered interesting because any researcher who is interested in this data set could do that, and the fact that no one has indicates that people don't currently care to. However, if you use a known algorithm to find an unexpected or surprising result then your project might still be publishable, and the more surprising or interesting the conclusions you can draw are, the more publishable the algorithm is. Here the strength of your paper is going to be determined by the data analysis you do, and how you show that your work is connected to work people are already interested in.

For example, I do a lot of research on social networks, and one hot topic in social network analysis is the detecting of communities inside of a broader social network. For the second approach, if you create a new algorithm for community detection, you can write a paper about your new algorithm comparing it to old algorithms and pointing out data sets where your algorithm is preferred. If you take a known community detection algorithm and use it to show that communities in certain types of graphs have an interesting property, that would be publishable. Additionally, you can somewhat blend these two cases if you were to take a known algorithm and show how it can be used for community detection, while everyone else is using it for some other purpose.