LEADER 03387nam 2200673Ia 450 001 9910808921203321 005 20230617015336.0 010 $a1-281-34776-0 010 $a9786611347765 010 $a981-256-540-X 010 $a1-4237-2302-3 035 $a(CKB)1000000000033230 035 $a(EBL)238334 035 $a(OCoLC)475947809 035 $a(SSID)ssj0000135023 035 $a(PQKBManifestationID)11157921 035 $a(PQKBTitleCode)TC0000135023 035 $a(PQKBWorkID)10057434 035 $a(PQKB)10713740 035 $a(MiAaPQ)EBC238334 035 $a(WSP)00005210 035 $a(Au-PeEL)EBL238334 035 $a(CaPaEBR)ebr10088379 035 $a(CaONFJC)MIL134776 035 $a(EXLCZ)991000000000033230 100 $a20040930d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aData mining in time series databases /$feditors, Mark Last, Abraham Kandel, Horst Bunke 205 $a57th ed. 210 $aNew Jersey ;$aLondon $cWorld Scientific$dc2004 215 $a1 online resource (205 p.) 225 1 $aSeries in machine perception and artificial intelligence ;$vv. 57 300 $aDescription based upon print version of record. 311 $a981-238-290-9 320 $aIncludes bibliographical references. 327 $aPreface; 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 327 $aChapter 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 330 $aAdding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings. 410 0$aSeries in machine perception and artificial intelligence ;$vv. 57. 606 $aData mining 606 $aDistributed databases 615 0$aData mining. 615 0$aDistributed databases. 676 $a006.33 686 $a54.64$2bcl 701 $aLast$b Mark$01628015 701 $aKandel$b Abraham$028590 701 $aBunke$b Horst$028587 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910808921203321 996 $aData mining in time series databases$94101536 997 $aUNINA