000 03635nam a22005175i 4500
001 978-3-319-03410-2
003 DE-He213
005 20200421111656.0
007 cr nn 008mamaa
008 131206s2014 gw | s |||| 0|eng d
020 _a9783319034102
_9978-3-319-03410-2
024 7 _a10.1007/978-3-319-03410-2
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aAdhikari, Animesh.
_eauthor.
245 1 0 _aData Analysis and Pattern Recognition in Multiple Databases
_h[electronic resource] /
_cby Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXV, 238 p. 97 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v61
505 0 _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.
520 _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.
650 0 _aEngineering.
650 0 _aData mining.
650 0 _aPattern recognition.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aPattern Recognition.
650 2 4 _aData Mining and Knowledge Discovery.
700 1 _aAdhikari, Jhimli.
_eauthor.
700 1 _aPedrycz, Witold.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319034096
830 0 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v61
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-03410-2
912 _aZDB-2-ENG
942 _cEBK
999 _c54679
_d54679