Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
HARP: A Practical Projected Clustering Algorithm
IEEE Transactions on Knowledge and Data Engineering
SCHISM: A New Approach for Interesting Subspace Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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In this paper, we study the quality issue of subspace clusters, which is an important but unsolved challenge in the literature of subspace clustering. After binning the data set into disjoint grids/regions, current solutions of subspace clustering usually invoke a grid-based apriori-like procedure to efficiently identify dense regions level by level according to the monotonic property in so defined subspace regions. A cluster in a subspace is intuitively considered as a set of dense regions that each one is connected to another dense region in the cluster. The measure of what is a dense region is successfully studied in recent years. However, the rigid definition of subspace clusters as connected regions still needs further justification in terms of the two principal measures of clustering quality, i.e., the intra-cluster similarity and the inter-cluster dissimilarity. A true cluster is likely to be separated into two or more clusters, whereas many true clusters may be merged into a fat cluster. In this paper, we propose an innovative algorithm, called the QASC algorithm (standing for Quality-Aware Subspace Clustering) to effectively discover accurate clusters. The QASC algorithm is devised as a general solution to partition dense regions into clusters and can be easily integrated into most of grid-based subspace clustering algorithms. By conducting on extensive synthetic data sets, the experimental results reveal that QASC is effective in identifying true subspace clusters.