LEADER 03472oam 2200505 450 001 9910299480003321 005 20190911112725.0 010 $a3-319-03410-3 024 7 $a10.1007/978-3-319-03410-2 035 $a(OCoLC)877107070 035 $a(MiFhGG)GVRL6XRH 035 $a(EXLCZ)993710000000074980 100 $a20131025d2014 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aData analysis and pattern recognition in multiple databases /$fAnimesh Adhikari, Jhimli Adhikari, Witold Pedrycz 205 $a1st ed. 2014. 210 1$aCham, Switzerland :$cSpringer,$d2014. 215 $a1 online resource (xv, 238 pages) $cillustrations 225 1 $aIntelligent Systems Reference Library,$x1868-4394 ;$v61 300 $a"ISSN: 1868-4394." 311 $a3-319-03409-X 320 $aIncludes bibliographical references and index. 327 $aFrom the Contents: Synthesizing Different Extreme Association Rules in Multiple Data Sources -- Clustering items in time-stamped databases induced by stability -- Mining global patterns in multiple large databases -- Clustering Local Frequency Items in Multiple Data Sources -- Mining Patterns of Select Items in Different Data Sources. 330 $aPattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyse them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery, and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments. 410 0$aIntelligent systems reference library ;$vvolume 61. 606 $aData mining 606 $aEngineering 606 $aOptical pattern recognition 615 0$aData mining. 615 0$aEngineering. 615 0$aOptical pattern recognition. 676 $a006.3 700 $aAdhikari$b Animesh$4aut$4http://id.loc.gov/vocabulary/relators/aut$0720955 702 $aAdhikari$b Jhimli 702 $aPedrycz$b Witold$f1953- 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910299480003321 996 $aData Analysis and Pattern Recognition in Multiple Databases$91921631 997 $aUNINA