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[null,"1.","Record Nr.",null,"UNINA9910783223703321"]
[null,null,"Titolo",null,"Data mining in time series databases [[electronic resource] /] / editors, Mark Last, Abraham Kandel, Horst Bunke"]
[null,null,"Pubbl/distr/stampa",null,"New Jersey ; ; London, : World Scientific, c2004"]
[null,null,"ISBN",null,"1-281-34776-0"]
[{"$nulls":4},"9786611347765"]
[{"$nulls":4},"981-256-540-X"]
[{"$nulls":4},"1-4237-2302-3"]
[null,null,"Edizione",null,"[57th ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (205 p.)"]
[null,null,"Collana",null,"Series in machine perception and artificial intelligence ; ; v. 57"]
[null,null,"Classificazione",null,"54.64"]
[null,null,"Disciplina",null,"006.33"]
[null,null,"Soggetti",null,"Data mining"]
[{"$nulls":4},"Distributed databases"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Preface; Contents; Chapter 1 Segmenting Time Series: A Survey and Novel Approach E. Keogh, S. Chu, D. Hart and M. Pazzani; Chapter 2 A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences M. L. Hetland; Chapter 3 Indexing of Compressed Time Series E. Fink and K. B. Pratt; Chapter 4 Indexing Time-Series under Conditions of Noise M. Vlachos, D. Gunopulos and G. Das; Chapter 5 Change Detection in Classification Models Induced from Time Series Data G. Zeira, O. Maimon, M. Last and L. Rokach"]
[{"$nulls":4},"Chapter 6 Classification and Detection of Abnormal Events in Time Series of Graphs H. Bunke and M. KraetzlChapter 7 Boosting Interval-Based Literals: Variable Length and Early Classification C. J. Alonso Gonzalez and J. J. Rodriguez Diez; Chapter 8 Median Strings: A Review X. Jiang, H. Bunke and J. Csirik"]
[null,null,null,"Sommario/riassunto","Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings."]