A novel approach to revealing positive and negative co-regulated genes
Journal of Computer Science and Technology
Continuous subspace clustering in streaming time series
Information Systems
Maximal Subspace Coregulated Gene Clustering
IEEE Transactions on Knowledge and Data Engineering
On mining micro-array data by Order-Preserving Submatrix
International Journal of Bioinformatics Research and Applications
Discovering pattern-based subspace clusters by pattern tree
Knowledge-Based Systems
CoBi: Pattern Based Co-Regulated Biclustering of Gene Expression Data
Pattern Recognition Letters
GPUMAFIA: efficient subspace clustering with MAFIA on GPUs
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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Unlike traditional clustering methods that focus ongrouping objects with similar values on a set of dimensions,clustering by pattern similarity finds objects thatexhibit a coherent pattern of rise and fall in subspaces.Pattern-based clustering extends the concept of traditional clustering and bene ts a wide range of applications, including large scale scientific data analysis, targetmarketing, web usage analysis, etc. However, state-of-the-art pattern-based clustering methods (e.g., the pCluster algorithm) can only handle datasets of thousands ofrecords, which makes them inappropriate for many real-life applications. Furthermore, besides the huge data volume, many data sets are also characterized by their sequentiality, for instance, customer purchase records andnetwork event logs are usually modeled as data sequences.Hence, it becomes important to enable pattern-based clustering methods i) to handle large datasets, and ii) to discover pattern similarity embedded in data sequences.In this paper, we present a novel algorithm that offersthis capability. Experimental results from both real lifeand synthetic datasets prove its effectiveness and efficiency.