examples/chat_examples/example_1.json

{
  "query": "Assess DBSCAN, Dense cells and CLIQUE with appropriate steps. (8 marks)",
  "context": "DBSACN\nStep 1: Randomly select a point p. Compute distance between P and ail other points '\nStep 2: Find all points]from p with respect to its neighbourhoud and check whether it has minimum number of points m. If 80, it is marked as a core point\nStep 3: If it is a core point, then a new cluster is formed, or existing cluster 1s enlarged.\nStep 4: [fit is a border point, then the algorithm moves to the next point and marks it as visited\nStep 5: If it is a noise point, they are removed.\nStep 6: Merge the clusters if it is mergeable, dist (cc )< ¢.\nStep 7: Repeat the process 3-6 till all Points are processed. \n\nDense Cell\nStep 1: Defining a set of grid points and assigning the given data points on the grid.\nStep 2: Determine the dense and sparse cells. lf the number of points in a cell exceeds the threshold\nvalue t, the cell is categorized as a dense cell. Sparse cells are removed from the list.\nStep 3: Merge the dense cells if they are adjacent.\nStep 4: Form a list of grid cells for every subspace as output.\n\nCLIQUE\nStage 1\nStep 1: Identify the dense cells\nStep 2: Merge dense cells c. and c, if they share the same interval.\nStep 3: Generate Apriori rule to generate (k + 1)\" cell tor higher dimension. Then, check\nwhether the number of points across the threshold This 1s repeated till there are no\ndense cells or a new generation of dense cells\n\nStage 2\nStep 1: Merging of dense cells into a cluster is carried out in each subspace using maximal regions to cover dense cells The maximal region is a hyperrectangle where all cells fall into.\nStep 2; Maximal region tries to cover all dense cells to form clusters.",
  "rules": "- If the question says answer for X number of marks, you have to provide X number of points.\n - Each point has to be explained in 3-4 sentences.\n - In case the context express a mathematical equation, provide the equation in LaTeX format as shown in the example.\n - In case the user requests for a code snippet, provide the code snippet in the language specified in the example.- If the user requests to summarise or use the previous message as context ignoring the explicit context given in the message.",
  "answer": "# Dense Cells\nStep 1: Define a set of grid points and assign the given data points on the grid.\nStep 2: Determine the dense and sparse cells. If the number of points in a cell exceeds the threshold value t, the cell is categorized as dense cell. Sparse cells are removed from the list.\nStep 3: Merge the dense cells if they are adjacent.\nStep 4: Form a list of grid cells for every subspace as output.\n\n#CILQUE\n **Stage 1:**\n- Step 1: Identify the dense cells.\n- Step 2: Merge dense cells c₁ and c₂ if they share the same interval.\n- Step 3: Generate a particle rule to generate (k + 1)th cell for higher dimension. Then, check whether the number of points cross the threshold. This is repeated till there are no dense cells or new generation of dense cells.\n\n**Stage 2:**\n- Step 1: Merging of dense cells into a cluster is carried out in each subspace using maximal regions to cover dense cells. The maximal region is an hyperrectangle where all cells fall into.\n- Step 2: Maximal region tries to cover all dense cells to form clusters.\n\n # DBSCAN\n- Step 1: Randomly select a point p. Compute distance between p and all other points.\n- Step 2: Find all points from p with respect to its neighborhood and check whether it has minimum number of points m. If so, it is marked as a core point.\n- Step 3: If it is a core point, then a new cluster is formed, or existing cluster is enlarged.\n- Step 4: If it is a border point, then the algorithm moves to the next point and marks it as visited.\n- Step 5: If it is a noise point, they are removed.\n- Step 6: Merge the clusters if it is mergeable, dist (c, c) < ɛ.\n- Step 7: Repeat the process 3-6 till all points are processed."
}