48,987 research outputs found

    Overlapped Text Partition Algorithm for Pattern Matching on Hypercube Networked Model

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    The web has been continuously growing and getting hourglass shape. The indexed web is measured to contain at least 30 billion pages. It is no surprise that searching data poses serious challenges in terms of quality and speed. Another important subtask of the pattern discovery process is sting matching, where in which the pattern occurrence is already known and we need determine how often and where it is occurs in given text. The target of current research challenges and identified the new trends i.e distributed environment where in which the given text file is divided into subparts and distributed to N no. of processors organized in hypercube networked fashion .To improve the search speed and reduce the time complexity we need to run the string matching algorithms in parallel distributed environment called as hypercube networked model using RMI method. we considered both KV-KMP and KV-boyer-moore string matching algorithms for pattern matching in large text data bases using three data sets and graph's drawn for different patterns

    Frequent Subgraph Mining in Outerplanar Graphs

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    In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we define the class of so called tenuous outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for tenuous outerplanar graphs that works in incremental polynomial time, and evaluate the algorithm empirically on the NCI molecular graph dataset

    Frequent Subgraph Mining in Outerplanar Graphs

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    In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we define the class of so called tenuous outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for tenuous outerplanar graphs that works in incremental polynomial time, and evaluate the algorithm empirically on the NCI molecular graph dataset

    Using Answer Set Programming for pattern mining

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    Serial pattern mining consists in extracting the frequent sequential patterns from a unique sequence of itemsets. This paper explores the ability of a declarative language, such as Answer Set Programming (ASP), to solve this issue efficiently. We propose several ASP implementations of the frequent sequential pattern mining task: a non-incremental and an incremental resolution. The results show that the incremental resolution is more efficient than the non-incremental one, but both ASP programs are less efficient than dedicated algorithms. Nonetheless, this approach can be seen as a first step toward a generic framework for sequential pattern mining with constraints.Comment: Intelligence Artificielle Fondamentale (2014
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