Uncertainty Handling and Quality Assessment in Data Mining
Nonfiction, Computers, Advanced Computing, Theory, General Computing, Internet
| Author: | Michalis Vazirgiannis, Maria Halkidi, Dimitrious Gunopulos | ISBN: | 9781447100317 |
| Publisher: | Springer London | Publication: | December 6, 2012 |
| Imprint: | Springer | Language: | English |
| Author: | Michalis Vazirgiannis, Maria Halkidi, Dimitrious Gunopulos |
| ISBN: | 9781447100317 |
| Publisher: | Springer London |
| Publication: | December 6, 2012 |
| Imprint: | Springer |
| Language: | English |
The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.
The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.
