Data Mining

A. A set of databases from different vendors, possibly using different database paradigms
B. An approach to a problem that is not guaranteed to work but performs well in most cases
C. Information that is hidden in a database and that cannot be recovered by a simple SQL query.
D. None of these

A. A stage of the KDD process in which new data is added to the existing selection.
B. The process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them
C. The distance between two points as calculated using the Pythagoras theorem
D. None of these

A. It is hidden within a database and can only be recovered if one is given certain clues (an example IS encrypted information).
B. The process of executing implicit previously unknown and potentially useful information from data
C. An extremely complex molecule that occurs in human chromosomes and that carries genetic information in the form of genes.
D. None of these

A. Combining different types of method or information
B. Approach to the design of learning algorithms that is structured along the lines of the theory of evolution.
C. Decision support systems that contain an information base filled with the knowledge of an expert formulated in terms of if-then rules.
D. None of these

A. Additional acquaintance used by a learning algorithm to facilitate the learning process
B. A neural network that makes use of a hidden layer
C. It is a form of automatic learning.
D. None of these

A. It uses machine-learning techniques. Here program can learn from past experience and adapt themselves to new situations
B. Computational procedure that takes some value as input and produces some value as output.
C. Science of making machines performs tasks that would require intelligence when performed by humans
D. None of these

A. The amount of information with in data as opposed to the amount of redundancy or noise
B. One of the defining aspects of a data warehouse
C. Restriction that requires data in one column of a database table to the a subset of another-column
D. None of these

A. Machine-learning involving different techniques
B. The learning algorithmic analyzes the examples on a systematic basis and makes incremental adjustments to the theory that is learned
C. Learning by generalizing from examples
D. None of these

A. A component of a network
B. In the context of KDD and data mining, this refers to random errors in a database table.
C. One of the defining aspects of a data warehouse
D. None of these

A. Non-trivial extraction of implicit previously unknown and potentially useful information from data
B. Set of columns in a database table that can be used to identify each record within this table uniquely.
C. collection of interesting and useful patterns in a database
D. none of these

A. A class of learning algorithms that try to derive a Prolog program from examples
B. A table with n independent attributes can be seen as an n- dimensional space.
C. A prediction made using an extremely simple method, such as always predicting the same output.
D. None of these

A. Additional acquaintance used by a learning algorithm to facilitate the learning process
B. A neural network that makes use of a hidden layer
C. It is a form of automatic learning.
D. None of these

A. Additional acquaintance used by a learning algorithm to facilitate the learning process
B. A neural network that makes use of a hidden layer
C. It is a form of automatic learning.
D. None of these

A. A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory.
B. Any mechanism employed by a learning system to constrain the search space of a hypothesis
C. An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation.
D. None of these

A. Online wallets
B. Coinbase
C. Hardware wallet
D. Nonce