In many real world problems, data are collected in high dimensional space. Detecting clusters in high dimensional spaces are a challenging task in the data mining problem. Subspace clustering is an emerging method, which instead of finding clusters in the full space, it finds clusters in different subspace of the original space. Subspace clustering has been successfully applied in various domains. Recently, the proliferation of high-dimensional data and the need for quality clustering results have moved the research era to enhanced subspace clustering, which targets on problems that cannot be handled or solved effectively through traditional subspace clustering. These enhanced clustering techniques involves in handling the complex data and improving clustering results in various domains like social networking, biology, astronomy and computer vision. The authors have reviewed on the enhanced subspace clustering paradigms and their properties. Mainly they have discussed three main problems of enhanced subspace clustering, first: overlapping clusters mined by significant subspace clusters. Second: overcome the parameter sensitivity problems of the state-of-the-art subspace clustering algorithms. Third: incorporate the constraints or domain knowledge that can make to improve the quality of clusters. They also discuss the basic subspace clustering, the relevant high-dimensional clustering approaches, and describes how they are related.