| 000 | 02911nam a22003855i 4500 | ||
|---|---|---|---|
| 001 | 111261 | ||
| 003 | ES-MaUEC | ||
| 005 | 20230102113503.0 | ||
| 006 | a||||fo|||| 00| 0 | ||
| 007 | cr nn nnnaamaa | ||
| 008 | 190110s2019 gw a o |||| 0|eng d | ||
| 020 |
_a9783030051273 _9 |
||
| 024 | 7 |
_a10.1007/978-3-030-05127-3 _2doi |
|
| 040 |
_bspa _dES-MaUEC _cES-MaUEC |
||
| 050 | 4 |
_aQA76.9.D343 _b2019 EB |
|
| 100 | 1 |
_aRanga Suri, N. N. R. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 245 | 1 | 0 |
_aOutlier Detection : _bTechniques and Applications : _bA Data Mining Perspective _cby N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan. |
| 264 | 1 |
_aCham _bSpringer International Publishing : _bImprint: Springer _c2019. |
|
| 300 |
_a1 recurso en línea (XXII, 214 páginas) _b 48 ilustraciones,3 ilustraciones a color |
||
| 347 |
_atext file _bPDF |
||
| 490 | 0 |
_aIntelligent Systems Reference Library _x1868-4394 _v155 |
|
| 505 | 0 | _aIntroduction -- Outlier Detection -- Research Issues in Outlier Detection -- Computational Preliminaries -- Outlier Detection in Categorical Data -- Outliers in High Dimensional Data. | |
| 520 | 3 | _aThis book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges. | |
| 650 | 7 |
_aData mining _2embne _9162648 |
|
| 700 | 1 |
_aMurty M, Narasimha. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 700 | 1 |
_aAthithan, G. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 710 | 2 |
_aSpringerLink (Online service) _9106996 |
|
| 776 | 0 | 8 |
_iPrinted edition: _z9783030051259 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030051266 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-05127-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 490 | 0 | _aIntelligent Technologies and Robotics (Springer-42732) | |
| 942 |
_2lcc _cLE |
||
| 988 | _aPrimersemestre_2019_Robotics | ||
| 998 |
_aSI _a_alco _a_vill _b09/2019 _cm _dz _ea _feng _ggw _h0 |
||
| 999 |
_c111261 _d111261 _x1 |
||