A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences

Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method

Mining Order-Preserving Submatrices from Data with Repeated Measurements

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On mining micro-array data by order-preserving submatrix

We study the problem of pattern-based subspace clustering. Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent pattern of rises and falls in subspaces. Applications of pattern-based subspace clustering include DNA micro-array data analysis, automatic recommendation systems and target marketing systems. Our goal is to devise pattern-based clustering methods that are capable of (1) discovering useful patterns of various shapes, and (2) discovering all significant patterns. We argue that previous solutions in pattern-based subspace clustering do not satisfy both requirements. Our approach is to extend the idea of Order-Preserving Submatrix (or OPSM). We devise a novel algorithm for mining OPSM, show that OPSM can be generalized to cover most existing pattern-based clustering models, and propose a number of extension to the original OPSM model. © 2005 IEEE.link_to_subscribed_fulltex

On mining micro-array data by order-preserving submatrix

We study the problem of pattern-based subspace clustering which is clustering by pattern similarity finds objects that exhibit a coherent pattern of rises and falls in subspaces. Applications of pattern-based subspace clustering include DNA micro-array data analysis. Our goal is to devise pattern-based clustering methods that are capable of • discovering useful patterns of various shapes • discovering all significant patterns. Our approach is to extend the idea of Order-Preserving Submatrix (OPSM). We devise a novel algorithm for mining OPSM, show that OPSM can be generalised to cover most existing pattern-based clustering models and propose a number of extensions to the original OPSM model. Copyright © 2007 Inderscience Enterprises Ltd.link_to_subscribed_fulltex

On mining micro-array data by order-preserving submatrix

We study the problem of pattern-based subspace clustering. Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent pattern of rises and falls in subspaces. Applications of pattern-based subspace clustering include DNA micro-array data analysis, automatic recommendation systems and target marketing systems. Our goal is to devise pattern-based clustering methods that are capable of (1) discovering useful patterns of various shapes, and (2) discovering all significant patterns. We argue that previous solutions in pattern-based subspace clustering do not satisfy both requirements. Our approach is to extend the idea of Order-Preserving Submatrix (or OPSM). We devise a novel algorithm for mining OPSM, show that OPSM can be generalized to cover most existing pattern-based clustering models, and propose a number of extension to the original OPSM model