Combinatorial pattern discovery for scientific data: some preliminary results
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
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SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Extracting Partial Structures from HTML Documents
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ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
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CPM '01 Proceedings of the 12th Annual Symposium on Combinatorial Pattern Matching
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DS '98 Proceedings of the First International Conference on Discovery Science
Mining Semi-structured Data by Path Expressions
DS '01 Proceedings of the 4th International Conference on Discovery Science
Discovering instances of poetic allusion from anthologies of classical Japanese poems
Theoretical Computer Science
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Information and Software Technology
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The rapid progress of computer and network technologies makes it easy to collect and store a large amount of unstructured or semi-structured texts such as Web pages, HTML/XML archives, E-mails, and text files. These text data can be thought of large scale text databases, and thus it becomes important to develop an efficient tools to discover interesting knowledge from such text databases.There are a large body of data mining researches to discover interesting rules or patterns from well-structured data such as transaction databases with boolean or numeric attributes [1,8,13]. However, it is difficult to directly apply the traditional data mining technologies to text or semi-structured data mentioned above since these text databases consist of (i) heterogeneous and (ii) huge collections of (iii) un-structured or semi-structured data. Therefore, there still have been a small number of studies on text mining, e.g., [4,5,12,17].Our research goal is to devise an efficient semi-automatic tool that supports human discovery from large text databases. Therefore, we require a fast pattern discovery algorithm that can work in time, e.g., O(n) to O(n log n), to respond in real time on an unstructured data set of total size n. Furthermore, such an algorithm has to be robust in the sense that it can work on a large amount of noisy and incomplete data without the assumption of an unknown hypothesis class.