High-dimensional data with lots of features makes clustering difficult. Existing algorithms struggle with this complexity, leading to poor performance. Researchers have been working on new clustering techniques to handle high-dimensional data better. Different clustering methods like subspace, model-based, density-based, and partition-based have been developed to address this issue. Each method has its own strengths and weaknesses. Future work will focus on improving these algorithms to handle high-dimensional data more effectively.