Advances in Knowledge Discovery in Databases

Name: Advances in Knowledge Discovery in Databases
Brand: Springer International Publishing AG
SKU: 9783319132112
Price: 427.61 PLN
Availability: InStock

Autor: Animesh Adhikari, Jhimli Adhikari

Język:

Oprawa: Twarda

Wydawca: Springer International Publishing AG

Dostępność: Dostępna u dostawcy

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Knowledge discovery in databases (KDD) is a well known research area in computer science. It has bee...

Informacje o książce

Autor

Animesh Adhikari, Jhimli Adhikari

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2014

strony

370

EAN

9783319132112

ISBN

3319132113

Enbook ID

09034230

Wydawca

Springer International Publishing AG

Waga

738

Wymiary

243 x 167 x 27

Pełny opis

Knowledge discovery in databases (KDD) is a well known research area in computer science. It has been marching ahead rapidly since its inception in late 1980s and has come to prominence over the last two and half decades as a discipline in its own right. With the advancements of hardware technologies, it is now feasible to collect large amount of data within a short span of time, and process them within a reasonable time limit. There are many areas of data mining that offer challenges and opportunities. This book presents some advancement in the areas on market basket database, time-stamped databases and multiple related databases. Market basket data analysis started at the beginning, and journey continues till now. Many interesting and intelligent algorithms are reported on data mining tasks. Also, a large number of association measures are invented over time, and that play significant roles in decision support applications. Mining time-stamped data has become a natural activity as real databases are dynamic, and hence grow over time, and it will continue to dominate in future. Time-based data analyses and identifying temporal patterns are two major activities in this domain. Mining multiple related databases is relatively a recent topic of data mining. Numerous problems are being reported in these days. Local patterns analysis provides a way to mine multiple large databases reasonably well. Most of the recent developments in these three domains are discussed, analyzed, and contrasted.§

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