Several application domains such as molecular biology and geography produce a tremendous amount of data which can no longer be managed without the help of efficient and effective data mining methods. One of the primary data mining tasks is clustering. However, traditional clustering algorithms often fail to detect meaningful clusters because most real-world data sets are characterized by a high dimensional, inherently sparse data space. Nevertheless, the data sets often contain interesting clusters which are hidden in various subspaces of the original feature space. Therefore, the concept of subspace clustering has recently been addressed, which aims at automatically identifying subspaces of the feature space in which clusters exist. In this paper, we introduce SUBCLU (density-connected Subspace Clustering), an effective and efficient approach to the subspace clustering problem. Using the concept of density-connectivity underlying the algorithm DBSCAN [EKSX96], SUBCLU is based on a formal clustering notion. In contrast to existing grid-based approaches, SUBCLU is able to detect arbitrarily shaped and positioned clusters in subspaces. The monotonicity of density-connectivity is used to efficiently prune subspaces in the process of generating all clusters in a bottom up way. While not examining any unnecessary subspaces, SUBCLU delivers for each subspace the same clusters DBSCAN would have found, when applied to this subspace separately.
In REF discussed the primary data mining tasks is clustering.
Density-Connected Subspace Clustering for High-Dimensional Data
SDM
Computer Science