diff --git a/CLUEstering/CLUEstering.hpp b/CLUEstering/CLUEstering.hpp
deleted file mode 100644
index 76882c3..0000000
--- a/CLUEstering/CLUEstering.hpp
+++ /dev/null
@@ -1,85 +0,0 @@
-
-#ifndef cluestering_hpp
-#define cluestering_hpp
-
-#include <algorithm>
-#include <cstdint>
-#include <fstream>
-#include <iostream>
-#include <string>
-#include <utility>
-
-#include "DataFormats/Points.h"
-#include "DataFormats/alpaka/PointsAlpaka.h"
-#include "CLUE/CLUEAlgoAlpaka.h"
-#include "CLUE/Run.h"
-
-using Dim = uint8_t;
-
-template <Dim ndim>
-class Clusterer {
-private:
-  float m_dc, m_rhoc, m_outlier;
-  uint32_t m_pointePerTile;
-  // Points SoA (Structure of Array)
-  Points<ndim> m_points;
-  /* std::unique_ptr<ConvolutionalKernel> m_kernel; */		// todo when kernels reworked
-  std::vector<std::vector<int>> m_clusterResults;
-
-public:
-  Clusterer() = delete;
-  Clusterer(float dc, float rhoc, float outlier, uint32_t pointsPerTile = 128)
-      : m_dc{dc}, m_rhoc{rhoc}, m_outlier{outlier}, m_pointePerTile{pointsPerTile} {}
-
-  template <typename Vec, typename... Vecs>		// todo: need to add a constraint on the template parameters
-  void read_data(Vec vec, Vecs... vecs) {
-    if constexpr (sizeof...(vecs) == 0) {
-      m_points.m_weight = vec;
-    } else {
-      std::for_each(vec.begin(), vec.end(), [&, this](auto& x) {
-        /* m_points.m_coords.push_back(VecArray<float, ndim>{x, vecs...}); */
-      });
-	  read_data(std::forward(vecs)...);
-    }
-  }
-
-  void run_clue(const std::string& backend,
-                std::size_t block_size = 256,
-                std::size_t device_id = 0,
-                bool verbose = false) {
-	if (backend == "cpu serial") {
-	  using namespace alpaka_serial_sync;
-	  const auto device = alpaka::getDevByIdx<Acc1D>(device_id);
-	  Queue queue(device);
-
-	  // note: alternatively just call run<Ndim, Kernel>
-	  CLUEAlgoAlpaka<Acc1D, ndim> algo(m_dc, m_rhoc, m_outlier, m_pointePerTile, queue);
-	  PointsAlpaka<ndim> d_points(queue, m_points.n);
-
-	  algo.make_clusters(m_points, d_points, ConvolutionalKernel(), queue, block_size);
-	} else if (backend == "cpu tbb") {
-	  using namespace alpaka_tbb_async;
-	} else if (backend == "gpu cuda") {
-	  using namespace alpaka_cuda_async;
-	} else if (backend == "gpu hip") {
-	  using namespace alpaka_rocm_async;
-	}
-  }
-
-  void to_csv(const std::string& pathToFile) {
-    std::fstream csvFile(pathToFile);
-    if (!csvFile.is_open()) {
-      throw std::runtime_error("Could not open file: " + pathToFile);
-    }
-
-    for (size_t i{}; i < m_points.n; ++i) {
-      for (size_t dim{}; dim < ndim; ++dim) {
-        csvFile << m_points.m_coords << ',';
-      }
-      csvFile << m_points.m_clusterIndex[i] << ',' << m_points.m_isSeed[i];
-      csvFile << '\n';
-    }
-  }
-};
-
-#endif