000 | 03284nam a22005775i 4500 | ||
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001 | 978-0-387-24247-7 | ||
003 | DE-He213 | ||
005 | 20161121230515.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2005 xxu| s |||| 0|eng d | ||
020 |
_a9780387242477 _9978-0-387-24247-7 |
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024 | 7 |
_a10.1007/b104937 _2doi |
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050 | 4 | _aQA76.9.D343 | |
072 | 7 |
_aUNF _2bicssc |
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072 | 7 |
_aUYQE _2bicssc |
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072 | 7 |
_aCOM021030 _2bisacsh |
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082 | 0 | 4 |
_a006.312 _223 |
100 | 1 |
_aWang, Wei. _eauthor. |
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245 | 1 | 0 |
_aMining Sequential Patterns from Large Data Sets _h[electronic resource] / _cby Wei Wang, Jiong Yang. |
264 | 1 |
_aBoston, MA : _bSpringer US, _c2005. |
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300 |
_aXV, 163 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aAdvances in Database Systems, _x1386-2944 ; _v28 |
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505 | 0 | _aRelated Work -- Periodic Patterns -- Statistically Significant Patterns -- Approximate Patterns -- Conclusion Remark. | |
520 | _aThe focus of Mining Sequential Patterns from Large Data Sets is on sequential pattern mining. In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences. This information has been of great interest for analyzing the sequential data to find its inherent characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. To meet the different needs of various applications, several models of sequential patterns have been proposed. This volume not only studies the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. Mining Sequential Patterns from Large Data Sets provides a set of tools for analyzing and understanding the nature of various sequences by identifying the specific model(s) of sequential patterns that are most suitable. This book provides an efficient algorithm for mining these patterns. Mining Sequential Patterns from Large Data Sets is designed for a professional audience of researchers and practitioners in industry and also suitable for graduate-level students in computer science. . | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aComputer communication systems. | |
650 | 0 | _aData structures (Computer science). | |
650 | 0 | _aDatabase management. | |
650 | 0 | _aData mining. | |
650 | 0 | _aInformation storage and retrieval. | |
650 | 0 | _aMultimedia information systems. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aDatabase Management. |
650 | 2 | 4 | _aInformation Storage and Retrieval. |
650 | 2 | 4 | _aData Structures. |
650 | 2 | 4 | _aMultimedia Information Systems. |
650 | 2 | 4 | _aComputer Communication Networks. |
700 | 1 |
_aYang, Jiong. _eauthor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9780387242460 |
830 | 0 |
_aAdvances in Database Systems, _x1386-2944 ; _v28 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/b104937 |
912 | _aZDB-2-SCS | ||
950 | _aComputer Science (Springer-11645) | ||
999 |
_c499798 _d499798 |