C++ and Python implementation of SNNDPC algorithm.

The Matlab version is moved to the branch MatlabImplementation.

If you use Windows:

1. Open a Visual Studio developer command prompt (prefer x64 native)
2. cd to project root SNNDPC/
3. cmake -DCMAKE_BUILD_TYPE=Release -G "NMake Makefiles" -S . -B build\release
4. cmake --build build\release --target Demo
5. build\release\Demo.exe

The demo runs on the S2 dataset.

If CMake complains about its version, modify SNNDPC/CMakeLists.txt:1 to fit your CMake version.

To use other datasets, see Customization

Some highlighted requirements.

• Python: 3.8
• Vcpkg
  • Intel TBB: Optional, for parallelization
  • OpenMP: Optional, for parallelization

To use other provided datasets in demo:

1. Modify variable pathData at SNNDPC/Demo.cpp:14.

   • Macro SOLUTION_DIR is the absolute path to SNNDPC/.

2. Modify variables k, n, d, and nc according to the paper

   Variable	Reference
   k	Table 4, column Arg-
   n	Table 2, column No. of records
   d	Table 2, column No. of attributes
   nc	Table 2, column No. of clusters

To use external datasets in Demo.cpp:

1. Make sure your dataset has exactly 3 columns: x, y, and label.
   • If you want to use more attributes, you need to edit the fscanf call at Demo.cpp:24.

To use external datasets in a custom runner:

1. Load your dataset into a (flattened) 1D C-style array data, shape [n×d].
   • For the logical 2D array, each row is a record, and each column is an attribute.
2. Include SNNDPC.hpp.
3. Call the SNNDPC() with parameters k, n, d, nc, and data.
4. The function will return two pointers, the centroids and the assignment.
   • Both are a 1D array.
   • Both are created by new, you need to manually delete them to prevent memory leak.

If you have any inquiries, please open an issue instead of sending emails directly to me. My email address on the paper is no longer frequently checked.