Density-Connected Subspace Clustering for High-Dimensional Data

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 fo...

• Type: Conference
• Year: 2004
• Where: SDM
• Authors: Peer Kröger, Hans-Peter Kriegel, Karin Kailing
• Keywords: Data Mining, SDM 2004, Subspace Clustering, Subspace Clustering Problem