Data Mining & Data Ware House
Data Mining & Data Ware House
51. KDD (Knowledge Discovery in Database) is referred to
- Non-trivial extrction of implicit previusly unknown and potentially useful information from dat (A)
- Set of columns in a database table that can be used to identify each record within this table uniquely.
- collection of interesting and useful patterns in a database
- None of these
52. Key is referred to
- Non-trival extrction of implicit previously unknown and potentially useful information from dat(A)
- Set of columns in a database table that can be used to identify each record within this tabel uniquely
- Collection of interesting and useful patterns in database
- None of these
53. Inductive learning is
- Machine-learning involving different techniques
- The learning algorithmic analyzes the examples on a systematic basis and makes incremental adjustments to the theory that is learned
- Learning by generalizing from examples
- None of these
54. Integrated is
- The amount of information with in data as opposed to the amount of redundancy or noise
- One of the defining aspects of a data warehouse
- Restriction that requires data in one column of a database table to the sub-set of another-column
- None of these
55. Knowledge engineering is
- The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system
- It automatically maps an external signal space into a system's internal representational space. They are useful in the performance of classification tasks
- A process where an individual learns how to carry out a certain task when situation in which the task cannot be carried out to a situation in which the same task under the same circumstances can be carried out.
- None of these
56. Kohonen self-organizing map referred to
- The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system
- It automatically maps an external signal space into a system's internal representational space. they are useful in the performance of classification tasks
- A process where an individual learns how a carry out a certain task when making a transition from a situation in which the task cannot be carried out to a situation in which the same task under the same circumstaces can be carried out.
- None of these
57. Learning is
- The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system
- It automatically maps an external signal space into a system's internal representational space. they are useful in the performance of classification tasks.
- A process where an individual learns how to carry out a certain task when making a transition from a situation in a situation in which the same task under the same circumstances can be carried out.
- None of these
58. Learning algorithm referrers to
- An algorithm that can learn
- A sub-discipline of computer science that deals with the design and imp-lementation of learning algorithms.
- A machine-learning approach that abstracts from the actual strategy of an individual algorithm and can therefore be applied to any other form of machine learning.
- None of these
59. Meta-learning is
- An algorithm that can learn
- A sub-discipline of computerscience that deals with the design and implementation of learning algorithms.
- A machine-learning approach that abstracts from the actual strategy of an invdividual algorithm and can therefore be applied to any other form of machine learning.
- None of these
60. Meta-learning is
- An algorithm that can learn
- A sub-discipline of computer science that deals with the design and implementation of learning algorithms.
- A machine-learning approach that abstracts from the actual strategy of an individual algorithm and can therefore be applied to any other form of machiner learning
- None of these