1. Neural Network: A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In essence, neural networks can be thought of as a simulation of the human brain's neurons used to process complex data inputs.

2. Data Mining: Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an essential process where intelligent methods are applied to extract data patterns.

3. Classification Tree: A classification tree is a type of decision tree that is used to predict a response that can take on a finite number of values (categorical or integer). It is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node holds a class label.

4. Association Rule: Association rule is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. An example of an association rule would be "If a customer buys eggs, they are 80% likely to also purchase milk."

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1. Neural Network: A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In essence, neural networks can be thought of as a simulation of the human brain's neurons used to process complex data inputs.

2. Data Mining: Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an essential process where intelligent methods are applied to extract data patterns.

3. Classification Tree: A classification tree is a type of decision tree that is used to predict a response that can take on a finite number of values (categorical or integer). It is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node holds a class label.

4. Association Rule: Association rule is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. An example of an association rule would be "If a customer buys eggs, they are 80% likely to also purchase milk."

Knowee AI · Accepted Answer

1. Neural Network: A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In essence, neural networks can be thought of as a simulation of the human brain's neurons used to process complex data inputs.

2. Data Mining: Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an essential process where intelligent methods are applied to extract data patterns.

3. Classification Tree: A classification tree is a type of decision tree that is used to predict a response that can take on a finite number of values (categorical or integer). It is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node holds a class label.

4. Association Rule: Association rule is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. An example of an association rule would be "If a customer buys eggs, they are 80% likely to also purchase milk."

Define Neural network, Data mining , Classification tree & association rule

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